Most popular SSRN papers over the last 12 months:Order by:
'P' Versus 'Q': Differences and Commonalities between the Two Areas of Quantitative Finance abstract There exist two separate branches of finance that require advanced quantitative techniques: the "Q" area of derivatives pricing, whose task is to "extrapolate the present"; and the "P" area of quantitative risk and portfolio management, whose task is to "model the future."
We briefly trace the history of these two branches of quantitative finance, highlighting their different goals and challenges. Then we provide an overview of their areas of intersection: the notion of risk premium; the stochastic processes used, often under different names and assumptions in the Q and in the P world; the numerical methods utilized to simulate those processes; hedging; and statistical arbitrage.
The Siren Song of Factor Timing abstract Everyone seems to want to time factors. Often the first question after an initial discussion of factors is “ok, what’s the current outlook?” And the common answer, “the same as usual,” is often unsatisfying. There is powerful incentive to oversell timing ability. Factor investing is often done at fees in between active management and cap-weighted indexing and these fees have been falling over time. Factor timing has the potential of reintroducing a type of skill-based “active management” (as timing is generally thought of this way) back into the equation. I think that siren song should be resisted, even if that verdict is disappointing to some. At least when using the simple “value” of the factors themselves, I find such timing strategies to be very weak historically, and some tests of their long-term power to be exaggerated and/or inapplicable.
My Factor Philippic abstract Arnott, Beck, Kalesnik, and West (2016) (ABKW) study smart beta or factor-based strategies and come to the following conclusions: (1) Aside from value, most popular factor strategies currently look expensive. (2) These expensive factor valuations portend lower future returns and a strong possibility of a future “factor crash” in which they go “horribly wrong.” And (3) many of these non-value factors were never real to start with because their historical performance was due to factor richening. That is, researchers mistook the one-time returns from factor richening for truly repeatable “structural alpha.” ABKW’s implied bottom line (their many protestations to only making modest recommendations aside): stick with value, dump the other factors. This essay elaborates on my response in Asness (2016). In summary: (1) I find non-value factor valuations moderately expensive, but not as expensive as ABKW. (2) I argue that ABKW exaggerate the power of factor timing by improperly using long-horizon regression techniques. More proper short-horizon regressions suggest some weak factor timing ability and given this predictability, I construct value-based tactical factor timing strategies to test them. Unfortunately, these strategies add little to portfolios that are already invested in the value factor. It turns out that this “newly” discovered timing tool is, yet again, mostly just a version of regular old value investing. And (3) I examine ABKW’s claim that factor richening drives much of non-value long-term factor performance and find that this very serious allegation about other researchers’ work is totally without merit. Overall, these results suggest that one should be wary of aggressive factor timing. Instead, investors are better off identifying factors they believe in, and staying diversified across them, unless we see far more extreme pricing than we do today.
All that Glitters Is Not Gold: Comparing Backtest and Out-of-Sample Performance on a Large Cohort of Trading Algorithms abstract When automated trading strategies are developed and evaluated using backtests on historical pricing data, there exists a tendency to overfit to the past. Using a unique dataset of 888 algorithmic trading strategies developed and backtested on the Quantopian platform with at least 6 months of out-of-sample performance, we study the prevalence and impact of backtest overfitting. Specifically, we find that commonly reported backtest evaluation metrics like the Sharpe ratio offer little value in predicting out of sample performance (R² < 0.025). In contrast, higher order moments, like volatility and maximum drawdown, as well as portfolio construction features, like hedging, show significant predictive value of relevance to quantitative finance practitioners. Moreover, in line with prior theoretical considerations, we find empirical evidence of overfitting – the more backtesting a quant has done for a strategy, the larger the discrepancy between backtest and out-of-sample performance. Finally, we show that by training non-linear machine learning classifiers on a variety of features that describe backtest behavior, out-of-sample performance can be predicted at a much higher accuracy (R² = 0.17) on hold-out data compared to using linear, univariate features. A portfolio constructed on predictions on hold-out data performed significantly better out-of-sample than one constructed from algorithms with the highest backtest Sharpe ratios.
Classification-Based Financial Markets Prediction Using Deep Neural Networks abstract Deep neural networks (DNNs) are powerful types of artificial neural networks (ANNs) that use several hidden layers. They have recently gained considerable attention in the speech transcription and image recognition community for their superior predictive properties including robustness to over fitting. However their application to algorithmic trading has not been previously researched, partly because of their computational complexity. This paper describes the application of DNNs to predicting financial market movement directions. In particular we describe the configuration and training approach and then demonstrate their application to back testing a simple trading strategy over 43 different Commodity and FX future mid-prices at 5-minute intervals. All results in this paper are generated using a C implementation on the Intel Xeon Phi co-processor which is 11.4x faster than the serial version and a Python strategy back testing environment both of which are available as open source code written by the authors.
Days to Cover and Stock Returns  abstract A crowded trade emerges when speculators' positions are large relative to the asset's liquidity, making exit difficult. We study this problem of recent regulatory concern by focusing on short-selling. We show that days to cover (DTC), the ratio of short interest to trading volume, measures the costliness of exiting crowded trades. Crowding is an important concern as short-sellers avoid illiquid stocks and require a premium to enter into such trades. A strategy shorting high DTC stocks and buying low DTC stocks generates a 1.2% monthly return. A comparably large days-to-cover effect exists on the long positions of levered hedge funds.
How Rigged are Stock Markets? Evidence from Microsecond Timestamps abstract We use new timestamp data from the two Securities Information Processors (SIPs) to examine SIP reporting latencies for quote and trade reports. Reporting latencies average 1.13 milliseconds for quotes and 22.84 milliseconds for trades. Despite these latencies, liquidity-taking orders gain on average $0.0002 per share when priced at the SIP-reported national best bid or offer (NBBO) rather than the NBBO calculated using exchanges’ direct data feeds. Trading surrounding SIP-priced trades shows little evidence that fast traders initiate these liquidity-taking orders to pick-off stale quotes. These findings contradict claims that fast traders systematically exploit traders who transact at the SIP NBBO.
Statement of Financial Disclosure and Conflict of Interest: Neither author has any financial interest or affiliation (including research funding) with any commercial organization that has a financial interest in the findings of this paper. The authors are grateful to the University of California, Berkeley School of Law, for providing general faculty research support.
The Wisdom of Twitter Crowds: Predicting Stock Market Reactions to FOMC Meetings via Twitter Feeds abstract With the rise of social media, investors have a new tool to measure sentiment in real time. However, the nature of these sources of data raises serious questions about its quality. Since anyone on social media can participate in a conversation about markets -- whether they are informed or not -- it is possible that this data may have very little information about future asset prices. In this paper, we show that this is not the case by analyzing a recurring event that has a high impact on asset prices: Federal Open Market Committee (FOMC) meetings. We exploit a new dataset of tweets referencing the Federal Reserve and shows that the content of tweets can be used to predict future returns, even after controlling for common asset pricing factors. To gauge the economic magnitude of these predictions, the authors construct a simple hypothetical trading strategy based on this data. They find that a tweet-based asset-allocation strategy outperforms several benchmarks, including a strategy that buys and holds a market index as well as a comparable dynamic asset allocation strategy that does not use Twitter information.
Protective Asset Allocation (PAA): A Simple Momentum-Based Alternative for Term Deposits abstract Since the financial crisis of 2008 and the recent (end of 2015) pull back, investors are searching for less risky investments. Therefore, there is a growing demand for low risk/absolute return portfolios. In this paper we describe a simple dual-momentum model (called Protective Asset Allocation or PAA) with a vigorous “crash protection” which might fit this bill. It is a tactical variation on the traditional 60/40 stock/bond portfolio where the optimal stock/bond mix is determined by multi-market breadth using dual momentum. We backtested the model with several global multi-asset ETF-proxies. Starting from Dec 1970 allows us to investigate the behavior of PAA in periods with rate hikes as well. The in-sample (Dec 1970-Dec 1992) and out-of-sample returns of the most protective variant of our PAA strategy satisfy our absolute return requirement without compromising high returns. This makes PAA an appealing alternative for a 1-year term deposit.
Statistical Industry Classification abstract We give complete algorithms and source code for constructing (multilevel) statistical industry classifications, including methods for fixing the number of clusters at each level (and the number of levels). Under the hood there are clustering algorithms (e.g., k-means). However, what should we cluster? Correlations? Returns? The answer turns out to be neither and our backtests suggest that these details make a sizable difference. We also give an algorithm and source code for building "hybrid" industry classifications by improving off-the-shelf "fundamental" industry classifications by applying our statistical industry classification methods to them. The presentation is intended to be pedagogical and geared toward practical applications in quantitative trading.
From 'Blockchain Hype' to a Real Business Case for Financial Markets abstract Introduction: Blockchain Hype vs Blockchain Seclusion?
There has been a lot of noise in the press about the great potential uses for financial markets of Bitcoin-related technology, that could be extracted from the Bitcoin world and applied to existing markets to increase efficiency dramatically. Later, there has been a lot of noise about the fact that there is no actual use but all boils down to a generic enthusiasm called Blockchain Hype, and Bitcoin is the only reality where such technology can be fruitfully used.
This paper shows that there are real business cases for improving financial markets based on the lesson learnt from cryptocurrencies, but, differently from what the hype-enthusiasts say, they are not application of a technology to the existing business model of financial markets. They are reforms of the business model itself. What needs to be exported from the world of cryptocurrencies are aspects of the market organization, inspiration for a different accounting and legal system, and some aspects of the technology. These can be a huge contribution towards more robust, efficient and stable markets, but the process cannot be immediate and effortless, and can only be achieved within a market-wide strategic view.
One crucial misunderstanding here is the idea that Blockchain Technology can be exported to financial markets as they are to make them more efficient. This is meaningless; Blockchain technology was created to change some trust-based business processes to make them less reliant on trust; without structural changes in this direction the best of Blockchain technology is lost and just the inefficiencies are left. This misunderstanding is the perfect partner of the idea that Blockchain technology cannot be used outside the Bitcoin world. This is equally meaningless; Bitcoin was created to attempt a level of independence from trust sufficient to allow players to be anonymous and do without any legal protection; other business solutions based on a level of trust intermediate between Bitcoin and traditional finance can use similar technology and yet be very different from Bitcoins. But we must ready to use the concept of trust in a totally different way, as a way to analyze the different parts of a business process and the reason for its current inefficiencies and risks.
In the next we develop these concepts first in a parallel analysis of cryptocurrencies and financial markets. Then we focus on a specific business case regarding the collateralization of financial derivatives, that we describe bottom-up including quantifiable benefits in reducing costs, capital and risk. It is an example where the use of cryptocurrency technology is not more important than the business ideas developed in the analysis of cryptocurrencies; yet it was unconceivable before examples of distributed ledgers, smart contracts and oracles were visible in marketplaces. In fact, it was first presented in Morini and Sams (2015), in an introduction of the Blockchain innovation for the derivatives world.
Securities Clearance and Settlement Systems: A Guide to Best Practices abstract How to assess securities clearance and settlement systems, based on international standards and best practices.
As an essential part of a nation's financial sector infrastructure, securities clearance and settlement systems must be closely integrated with national payment systems so that safety, soundness, certainty, and efficiency can be achieved at a cost acceptable to all participants. Central banks have paid considerable attention to payment systems, but securities clearance and settlement systems have only recently been subjected to rigorous assessment.
The Western Hemisphere Payments and Securities Clearance and Settlement Initiative (WHI), led by the World Bank and in cooperation with the Centro de Estudios Monetarios Latinoamericanos (CEMLA), gave Guadamillas and Keppler a unique opportunity to observe how various countries in Latin America and the Caribbean undertake securities clearance and settlement. To do so, Guadamillas and Keppler developed a practical and implementable assessment methodology covering key issues that affect the quality of such systems.
In this paper they discuss the objectives, scope, and content of a typical securities system, identify the elements that influence the system's quality, and show how their assessment methodology works. They focus on the development of core principles and minimum standards for integrated systems of payments and securities clearance and settlement.
Their paper fills a gap by providing an evaluation tool for assessors of such systems, especially those who must assess evolving systems in developing and transition economies. Essentially, an assessment involves a structured analysis to answer four related questions:
- What are the objective and scope of a securities clearance and settlement system?
- Who are the participants, what roles do they play, and what expectations do they have?
- What procedures are required to satisfy the participants' needs?
- What inherent risks are involved, and how can they be mitigated at an acceptable cost?
This paper - a product of the Finance Cluster, Latin America and the Caribbean Region, and Financial Sector Infrastructure, Financial Sector Development Department - is part of a larger effort in the Bank to assess payment systems and securities clearance and settlement systems in Latin America and the Caribbean. The authors may be contacted at email@example.com or firstname.lastname@example.org.
It Takes a Village to Maintain a Dangerous Financial System abstract I discuss the motivations and actions (or inaction) of individuals in the financial system, governments, central banks, academia and the media that collectively contribute to the persistence of a dangerous and distorted financial system and inadequate, poorly designed regulations. Reassurances that regulators are doing their best to protect the public are false. The underlying problem is a powerful mix of distorted incentives, ignorance, confusion, and lack of accountability. Willful blindness seems to play a role in flawed claims by the system’s enablers that obscure reality and muddle the policy debate.
Conflicts of Interest in Self-Regulation: Can Demutualized Exchanges Successfully Manage Them? abstract Carson examines the implications of demutualization of financial exchanges for their roles as self-regulatory organizations. Many regulators and exchanges believe that conflicts of interest increase when exchanges convert to for-profit businesses. Demutualization also changes the nature of an exchange's regulatory role as broker-dealers' ownership interests are reduced. These factors are leading to reduced regulatory roles for exchanges in many jurisdictions. The resulting changes have significant implications for regulation of financial markets, especially as exchanges are the only self-regulating organizations (SROs) in most countries. Major changes in the role of exchanges require a rethinking of the allocation of regulatory functions and the role of self-regulation, as well as stronger mechanisms to mitigate conflicts of interest.
Carson looks at the views of both exchanges and regulators on these issues in Asian, European, and North American jurisdictions where major exchanges have converted to for-profit businesses. He finds that views on the conflicts of interest faced by demutualized exchanges vary widely. In addition, the tools and processes used by exchanges and regulators to manage conflicts also differ significantly across jurisdictions. The author concludes that new and greater conflicts result from demutualization and canvasses the regulatory responses in the jurisdictions examined.
This paper - a product of the Financial Sector Operations and Policy Department - is part of a larger effort in the department to study the development of securities markets in emerging markets.
Market Risk Premium Used in 71 Countries in 2016: A Survey with 6,932 Answers abstract This paper contains the statistics of the Equity Premium or Market Risk Premium (MRP) used in 2016 for 71 countries. We got answers for more countries, but we only report the results for 71 countries with more than 8 answers. 54% of the MRP used in 2016 decreased (vs. 2015) and 38% increased.
Most previous surveys have been interested in the Expected MRP, but this survey asks about the Required MRP. The paper also contains the references used to justify the MRP, and comments from 46 persons.
The Effects of Usury Laws on Higher-Risk Borrowers abstract In this Article, we exploit a natural experiment -- an unexpected judicial decision -- to study the effects of state usury laws on consumer loans to higher-risk borrowers. In May 2015, the U.S. Court of Appeals for the Second Circuit issued a decision that, in effect, switched on the usury laws of three States, rendering those laws enforceable against owners of consumer loans that had previously been issued under the expectation that the usury laws were preempted by federal statute. Using proprietary data from three marketplace lending platforms, we study the decision’s effect on consumer credit markets.
We find that the court’s decision significantly impaired credit availability for riskier borrowers, shrinking loan issuances to borrowers with the lowest FICO scores. We see no evidence, however, of strategic defaults by borrowers in these markets, despite the fact that the decision suggests that their loans are unenforceable. We also examine secondary market trading in notes backed by non-current, potentially usurious loans in the Second Circuit, and find that the decision reduced the prices of those notes. We do not, however, find evidence of a similar price decrease for notes backed by potentially usurious loans that the borrower continues to pay on time - suggesting that investors do not anticipate an increase in strategic defaults as a result of the court’s decision.
Value Creation Thinking: Powerpoint Presentation abstract Long-term value creation begins with clarity about the purpose of the firm and about management's core responsibilities. Value creation is critically tied to how well management develops and maintains a knowledge-building culture. These ideas are plainly communicated in this PowerPoint presentation which summarizes my book, Value Creation Thinking. The presentation is well suited for classroom discussion and includes an explanation of the life-cycle valuation model, which is used extensively by money management firms worldwide. Also included are long-term, life-cycle charts of major firms that illustrate how managerial skill and competition interact to determine firms' long-term financial performance and, ultimately, shareholder returns.
Factor Investing with Smart Beta Indices abstract The added value of smart beta indices is known to be explained by exposures to established factor premiums, but does that make these indices suitable for implementing a factor investing strategy? This paper finds that the amount of factor exposure provided by popular smart beta strategies differs considerably, as does their degree of focus on a single target factor. It also provides insight into how ‘quality’ and ‘high dividend’ indices relate to academic factors. Smart beta indices exhibit a performance that is in line with the amount of factor exposure provided, but it seems that they do not unlock the full potential offered by factor premiums. Altogether, these results imply that factor investing with smart beta indices is not as straightforward as one might think.
Following the Money: Lessons from the Panama Papers, Part 1: Tip of the Iceberg abstract Widely known as the “Panama Papers,” the world’s largest whistleblower case to date consists of 11.5 million documents and involves a year-long effort by the International Consortium of Investigative Journalists to expose a global pattern of crime and corruption where millions of documents capture heads of state, criminals and celebrities using secret hideaways in tax havens. Involving the scrutiny by over 400 journalists worldwide, these documents reveal the offshore holdings of at least several hundred politicians and public officials, including the prime ministers of Iceland and Pakistan, the president of Ukraine, and the King of Saudi Arabia. More than 214,000 offshore entities appear in the leak, connected to people in more than 200 countries and territories.
Since these disclosures became public, national security implications already include abrupt regime change, and probable future political instability. It appears likely that important revelations obtained from these data will continue to be forthcoming for years to come. Presented here is Part 1 of what may ultimately constitute numerous-installment coverage of this important inquiry into the illicit wealth derived from bribery, corruption, and tax evasion. This article proceeds as follows. First, disclosures regarding the treasure trove of documents from the Panama-based law firm, Mossack Fonseca are reviewed. Second, is a discussion of the impact and cost of bribery and corruption to the global community. Third, I define and briefly explore issues surrounding “tax evasion.” Fourth, the impact of social media and technological change on transparency is discussed. Next, a few thoughts about implications for future research are offered.
Quantitative Style Investing abstract I introduce a systematic portfolio choice solution that significantly beats a benchmark market portfolio by an average of 34.2% per year after transaction costs. The corresponding annual Sharpe ratio is 1.97 per year compared to 0.42, over 4.7 times the size of the benchmark. A more conservative sample that excludes micro cap stocks yields an annual Sharpe ratio 2.18 times the benchmark. I construct my solution by applying multivariable cross-sectional regressions of six key stock characteristics, to aggregate forecasting signals from multiple sources. I apply simple filtering techniques to reduce estimation and sampling error, use only information known at time t, and predict expected returns. I validate the procedure by achieving commensurate results as prior studies when forming portfolios from decile sorts. However, by sorting stocks by expected returns into more extreme portfolios, i.e. 25 and 50 portfolios, I am able to further enhance performance gains over existing works.
Tax Uncertainty and Retirement Savings Diversification abstract We investigate the optimal savings decisions for investors with access to pre-tax (traditional) and post-tax (Roth) versions of tax-advantaged retirement accounts. The model features a progressive tax schedule and uncertainty over future tax rates. In this setting, traditional accounts are valuable for hedging retirement account performance and managing current income near tax bracket cutoffs, whereas Roth accounts allow investors to mitigate uncertainty over future tax schedules. The optimal asset location policy for most households involves diversifying between traditional and Roth vehicles, and, contrary to conventional advice, the largest economic benefits from Roth investments accrue to investors with high current income.
Volatility Modelling and Trading abstract We provide a practical and technical overview of volatility trading strategies:
1) The insight for the design and back-testing of systematic volatility strategies
2) Understanding of risk-reward trade-off and potential pitfalls of volatility strategies
We focus on systematic and rule-based trading strategies that can be marketed as an investable index or a proprietary strategy:
1) Delta-hedged strategies for capturing the volatility and skew risk-premiums
2) Without delta-hedge: CBOE and customized options buy-write indices
We overview important implementation aspects:
1) Measuring the historic realized volatility
2) Forecasting the expected realized volatility
3) Measuring and forecasting implied and realized skew
4) Computing option delta consistently with empirical dynamics
5) Analysis of transaction costs
6) Managing the tail-risk of short volatility strategies
Covered Interest Rate Parity Deviations in the Post-Crisis World abstract We find that deviations from the covered interest rate parity condition imply large, persistent, and systematic arbitrage opportunities in one of the largest asset markets in the world. Contrary to the common view, we show that these deviations for major currencies are not explained away by credit risk or transaction costs. Furthermore, these deviations are highly correlated with nominal interest rates in the cross section and in the time series, higher at quarter ends post-crisis, significantly correlated with other fixed-income spreads, and much lower after proxying for banks’ balance sheet costs. These empirical findings point to key frictions in financial intermediation and their interactions with global imbalances during the post-Global Financial Crisis period.
Dark Pools, High-Frequency Trading, and the Financial Transaction Tax: A Solution or Complication? abstract The implementation of a financial transaction tax has been the subject of debate for many years. Countries all across the globe have enacted various types of this tax and have seen different results depending upon factors such as the rate and the types of financial instruments affected by it. With the recent explosion of high-frequency transactions, discussion of enacting a financial transaction tax has begun to grow in both Europe and the United States.
This Article will look at the possible effects of enacting a financial transaction tax on the U.S. marketplace. Before this type of tax is put into place, we must first analyze what the results of previous experiences were, what results could be expected from an implementation in the United States, and whether these results align with the basic goals of taxation.
The idea of a financial transaction tax has recently been paired with the idea of regulating Wall Street. However, while the tax may assist to an extent in regulating the marketplace, it should not be viewed in this regard, but instead mainly as a tool to raise revenue. There may be adverse effects of such a tax in the short-term, but the true impact on the market should be viewed in respect to the long-term outcomes. Additionally, while transparency and stability are important in the marketplace, an over-regulation of high-frequency trading, and specifically the dark-pool market in which the exchanges occur, could end up distorting the market and eliminating investment volume.
Overall, the implementation of a financial transaction tax may prove beneficial to the United States in raising revenue in the long-term but not as a regulatory device in the marketplace.
Lecture Notes on Risk Management & Financial Regulation abstract This book contains lecture notes from the course "Risk Management" given at the University of Paris-Saclay/Evry. These lecture notes are divided into three parts. After an introductory chapter presenting the main concepts of risk management and an overview of the financial regulation (Basel I-IV, Solvency I-II, Dodd-Frank, UCTIS, etc.), the first part is dedicated to the risk management in the banking sector and consists of six chapters: market risk, credit risk, counter party credit risk and collateral risk, operational risk, liquidity risk and asset/liability management risk. We begin with the market risk, because it permits to introduce naturally the concepts of risk factor and risk measure and to define the risk allocation approach. For each chapter, we present the corresponding regulation framework and the risk management tools. The second part is dedicated to non-banking financial sectors with four chapters dedicated to insurance, asset management, investors and market infrastructure (including central counter parties). This second part ends with a fifth chapter on systemic risk and shadow banking system. The third part of these lecture notes develops the mathematical and statistical tools used in risk management. It contains seven chapters: risk model and derivatives hedging, statistical inference and model estimation, copula functions, extreme value theory, Monte Carlo simulation, stress testing methods and scoring models. Each chapter of these lectures notes is extensively illustrated by numerical examples and contains also tutorial exercises. Finally, a technical appendix completes the lecture notes and contains some important elements on numerical analysis.
Quality Investing – Industry Versus Academic Definitions abstract In this study we provide an overview of common quality definitions that are currently used in the industry and those used in academic studies, and we outline the differences between these definitions. We show that there is a large dispersion in the definitions that are used for the quality factor with ‘industry’ definitions ranging from return-on-equity and profit margins to leverage and earnings variability, and ‘academic’ definitions such as operating accruals, net stock issues, and gross profitability. We document large performance differences between the different quality definitions. While ‘academic’ definitions for quality all seem to have significant predictive power for stock returns above and beyond common factors, we do not find significant predictive power for individual ‘industry’ definitions. Our results have important implications for the design of investment vehicles that provide investors exposure to the quality factor.
Do the Rich Know Better? – University Endowment Return Inequality Revisited abstract This paper revisits capital return inequality across university endowments. It combines university-level data on endowment size, investment returns, and portfolio allocations into a unified dataset. Using panel data regression, we replicate Piketty (2014)’s finding of a strong impact of size on investment return. Everything else the same, the biggest endowment has a capital return 8% higher than the smallest endowment. How- ever, after adjusting for risk using Sharpe Ratios, the strong positive correlation turns negligible or even negative. This result suggests that the higher return of bigger endowments can be attributed to risk compensation rather than to an informational premium.
Market Discipline under Systemic Risk: Evidence from Bank Runs in Emerging Economies abstract Levy-Yeyati, Martinez Peria, and Schmukler show that systemic risk exerts a significant impact on the behavior of depositors, sometimes overshadowing their responses to standard bank fundamentals. Systemic risk can affect market discipline both regardless of and through bank fundamentals. First, worsening systemic conditions can directly threaten the value of deposits by way of dual agency problems. Second, to the extent that banks are exposed to systemic risk, systemic shocks lead to a future deterioration of fundamentals not captured by their current values. Using data from the recent banking crises in Argentina and Uruguay, the authors show that market discipline is indeed quite robust once systemic risk is factored in. As systemic risk increases, the informational content of past fundamentals declines. These episodes also show how few systemic shocks can trigger a run irrespective of ex-ante fundamentals. Overall, the evidence suggests that in emerging economies, the notion of market discipline needs to account for systemic risk.
This paper - a product of the Finance Team, Development Research Group - is part of a larger effort in the group to study market discipline.
Long-Only Style Investing: Don't Just Mix, Integrate abstract We investigate two popular approaches to long-only style investing that are often considered as potential starting points for smart beta investors: the “portfolio mix” that builds a style portfolio from standalone style portfolios and the “integrated portfolio” that integrates styles directly in the portfolio construction process. Our key finding is that integrating styles is a much more effective way to harvest long-only style premia. Compared to the portfolio mix, the integrated portfolio substantially improves returns and information ratio by avoiding stocks with offsetting style exposures and including stocks with balanced positive style exposures.
Sports Betting As a New Asset Class: Can a Sports Trader Beat Hedge Fund Managers from 2010-2016? abstract The authors investigate whether sports traders who systematically invest in sports betting strategies can outperform hedge fund managers and the S&P 500 from 1st January 2010 to 7th January 2016. The authors take a simple betting strategy based on Horse races in the UK and invest consistently on laying (betting on the event not to occur) the 4 favourite horses (with the lowest odds) in each race. They find the following:
(1) this type of horse racing strategy provide uncorrelated returns to the market;
(2) the strategy outperforms the Credit Suisse Hedge fund Index and S&P 500 Total returns on average for the last 6 years.
In conclusion, the authors find that Sports trading can provide an attractive option to investors as an alternative asset to generate excess returns which are uncorrelated to their existing portfolio.
Limitations of Quantitative Claims About Trading Strategy Evaluation abstract One of the key assumptions of quantitative trading strategy evaluation is that Type II errors (missed discoveries) are preferable to Type I errors (false discoveries.) However, practitioners have known for long that the statistical properties of some genuine trading strategies are often indistinguishable from those of random trading strategies. Therefore, any adjustments of statistics to guard against p-hacking increase Type II error unless the power of the test is high. At the same time, the power of the test is limited by insufficient samples and changing market conditions. Furthermore, genuine strategies with statistical properties that are similar to those of random strategies may overfit due to favorable market conditions but fail when market conditions change. These facts severely limit the effectiveness of quantitative claims about trading strategy evaluation. Practitioners have instead resorted to Monte Carlo simulations and stochastic modeling in an effort to increase the chances of identifying robust trading strategies but these methods also have severe limitations due to changing market conditions, selection bias and data snooping. In this paper we present two examples that demonstrate the limitation of quantitative evaluation of trading strategies and we claim that the most effective way of guarding against overfitting and selection bias is by limiting the applications of backtesting to a class of strategies that employ similar but simple predictors of price. We claim that determining when market conditions change is in many cases fundamentally more important than any quantitative claims about trading strategy evaluation.
Stochastic Portfolio Theory: A Machine Learning Perspective abstract In this paper we propose a novel application of Gaussian processes (GPs) to financial asset allocation. Our approach is deeply rooted in Stochastic Portfolio Theory (SPT), a stochastic analysis framework introduced by Robert E. Fernholz that aims at flexibly analysing the performance of certain investment strategies in stock markets relative to benchmark indices. In particular, SPT has exhibited some investment strategies based on company sizes that, under realistic assumptions, outperform benchmark indices with probability 1 over certain time horizons. Galvanised by this result, we consider the inverse problem that consists of learning (from historical data) an optimal investment strategy based on any given set of trading characteristics, and using a user-specified optimality criterion that may go beyond outperforming a benchmark index. Although this inverse problem is of the utmost interest to investment management practitioners, it can hardly be tackled using the SPT framework. We show that our machine learning approach learns investment strategies that considerably outperform existing SPT strategies in the US stock market.
Abnormal Stock Market Returns Around Peaks in VIX: The Evidence of Investor Overreaction? abstract Even though the VIX index was intended to be a measure of future volatility of the stock market, researchers argue that in reality VIX measures the investor sentiment. Anecdotal evidence suggests that peaks in VIX coincide with stock market bottoms followed by rallies, yet so far there have been no scientific evidence confirming this casual observation. In this paper we perform an event study of abnormal stock market returns around peaks in VIX and discuss our findings within the framework of behavioral finance theory. First of all, we detect peaks in VIX using formal turning-point identification procedures and provide detailed descriptive statistics of periods of rising and falling VIX. The results of our event study reveal strong evidence of the presence of abnormal stock market returns around peaks in VIX. We argue that the pattern of abnormal returns can be attributed to investor overreaction to bad news with subsequent correction. To validate our conjecture, we test whether the abnormal returns around peaks in VIX satisfy the two properties of overreaction. We find that the results of these empirical tests are consistent with the overreaction hypothesis. To further confirm the idea that the VIX index reflects the investor sentiment, we test the predictions of behavioral finance theory which postulates that investor sentiment affects various types of stocks to different degrees. In agreement with the theoretical predictions, we find evidence that over the event window around a peak in VIX the prices of large and value stocks undergo a relatively small downward correction, while the prices of more speculative small and growth stocks are corrected down to a higher degree. Our additional tests suggest that these cross-sectional differences cannot be explained by a set of standard risk factors.
Just a One Trick Pony? An Analysis of CTA Risk and Return abstract Recently a range of alternative risk premia products have been developed promising investors hedge fund/CTA like returns with higher liquidity, transparency and relatively low fees. The attractiveness of these products rests on the assumption that they can deliver similar returns. Using a novel reporting bias free sample of 3,419 CTA funds as a testing ground, our results suggest this assumption is questionable. We find that CTAs are not a homogenous group. We identify eight different CTA sub-strategies, each with very different sources of return and low correlation between sub-strategies. When we specify recently identified alternative risk premia as factors to examine the sources of return of CTAs, we find that these premia fail to explain between 56% and 86% of returns. Our results for CTAs suggest that while these new products may deliver on liquidity, transparency and fees, investors expecting hedge fund/CTA like returns may be disappointed.
New Firm Formation and Industry Growth: Does Having a Market- or Bank-Based System Matter? abstract Do industries that depend heavily on external finance grow faster in market-based or bank-based financial systems? Are new firms more likely to form in a bank-based or a market-based financial system? Beck and Levine find no evidence for the superiority of either market-based or bank-based financial systems for industries dependent on external financing. But they find overwhelming evidence that industries heavily dependent on external finance grow faster in economies with higher levels of financial development and with better legal protection for outside investors - including strong creditor and shareholder rights and strong contract enforcement mechanisms.
Financial development also stimulates the establishment of new firms, which is consistent with the Schumpeterian view of creative destruction. Financial development matters. That the financial system is bank-based or market-based offers little additional information.
This paper - a product of the Financial Sector Strategy and Policy Department - is part of a larger effort in the department to understand the link between financial development and economic growth.
Creating a More Efficient Financial System: Challenges for Bangladesh abstract While Bangladesh has embarked on a path to reform its financial system, most prominently by privatizing its government-owned banks, the Nationalized Commercial Banks (NCBs), a sustainable long-term expansion of the financial system requires a more substantial change in the role of government. Using recent research and international comparisons, this paper argues that the government should move from its role as an operator and arbiter in the financial system to a facilitator role. This implies not only divestment from government-owned banks, but also de-politicization of the licensing process and a market-based bank failure resolution framework that focuses on intermediation and not on the rescue of individual institutions. Most important, the government should move away from the implicit guarantee for depositors and owners to applying the existing limited explicit deposit insurance for depositors, while simultaneously relying more on market participants to monitor and discipline banks instead of micro-managing financial institutions. This redefinition of government's role should not be limited to the banking system, but applies to other segments of the financial system, such as capital markets and the micro-finance sector, and should be seen as an essential element in the governance reform agenda and in the movement from a relationship-based economy to a market and arms-length economy.
When the Walk is Not Random: Commodity Prices and Exchange Rates abstract We show that there is a distinct commodity-related driver of exchange rate movements, even at fairly high frequencies. Commodity prices predict exchange rate movements of 11 commodity-exporting countries in an in-sample panel setting for horizons up to two months. We also find evidence of systematic (pseudo) out-of-sample predictability, overturning the results of Meese and Rogoff (1983): information embedded in our country-specific commodity price indices clearly helps improving upon the predictive accuracy of the random walk in the majority of countries. We further show that the link between commodity prices and exchange rates is not driven by changes in global risk appetite or carry.
Asset Allocation: A Recommendation for Resolving the Collision between Theory and Practice abstract We examine the creation of a low-cost optimal risky portfolio that individual investors can easily construct and manage. We consider five index mutual funds and three precious metals that are easy for investors to trade. Collectively, the mutual funds track the returns of the entire U.S. equity market, 98% of foreign stocks, U.S. investment grade bonds, all domestic REITs, and emerging markets. The three precious metals are gold, platinum, and palladium. Because these mutual funds are available in ETF form, we provide optimization results with and without short selling. Optimization results differ greatly from conventional wisdom regarding optimal asset allocation.
Fiduciary Financial Advice to Retirement Savers: Don't Overlook the Prudent Investor Rule abstract Americans now hold trillions of dollars in individual retirement savings accounts, raising concerns about conflicts of interest among financial advisers who provide advice to retirement savers. Prompted by these concerns, in April 2016 the Department of Labor promulgated a rule that imposes on financial advisers to retirement savers “fiduciary” status under the Employee Retirement Income Security Act. The Department reasoned that the fiduciary duty of loyalty would protect retirement savers from conflicted investment advice. But in addition to a duty of loyalty, fiduciary status also imposes a duty of care. With respect to investment management, the fiduciary standard of care is governed by the “prudent investor rule,” which is grounded in modern portfolio theory and requires an overall investment strategy having risk and return objectives reasonably suited to the purpose of the investment account. This essay calls attention to the regulatory imposition of the prudent investor rule on financial advisers to retirement savers. The essay also canvasses the basic tenets of the prudent investor rule, highlighting its nature as principles-based rather than prescriptive, and the customary role of an investment policy statement in compliance by professional fiduciaries.
Demystifying Pairs Trading: The Role of Volatility and Correlation abstract This paper investigates how the two technical drivers, volatility and correlation, influence the algorithm of the investment strategy pairs trading. We model and empirically prove the connection between the rule-based pair selection, the trading algorithm, and the total return. Our insights explain why pairs trading profitability varies across markets, industries, macroeconomic circumstances, and firm characteristics. Furthermore, we critically evaluate the power of the traditionally applied pair selection procedure. In the US market, we find risk-adjusted monthly returns of up to 76bp for portfolios, which are double sorted on volatility and correlation between 1990 and 2014. Our findings are robust to liquidity issues, bid-ask spread, and limits of arbitrage.
Spread, Volatility, and Volume Relationship in Financial Markets and Market Maker's Profit Optimization abstract We study the relationship between price spread, volatility and trading volume. We find that spread forms as a result of interplay between order liquidity and order impact. When trading volume is small adding more liquidity helps improve price accuracy and reduce spread, but after some point additional liquidity begins to deteriorate price. The model allows to connect the bid-ask spread and high-low bars to measurable microstructural parameters and express their dependence on trading volume, volatility and time horizon. Using the established relations, we address the operating spread optimization problem to maximize the market-maker’s profit.
Can Sentiment Indicators Signal Market Reversals? abstract In this study we use machine learning algorithm to test Amareos sentiment indicator's predictive power of market reversals. We then build and test a viable trading strategy.
As input for the algorithm, we used eight market sentiment indicators (Anger, Anticipation, Disgust, Fear, Gloom, Joy, Optimism and Sentiment) on 20 major equity indices from January 1, 2005 to April 15, 2016.
As the target output, we use a classification of the performance of the indices on the following 182 days - approximately six months - split between bottom, top and neutral days.
Our learning algorithm is of the type called random forest. Through calibration on a training set composed of 64% of the data, we obtain a final set of decision trees, or forest. We then examine the out of sample accuracy of this forest on the remaining 36% of the data.
As the accuracy on the test set is relatively high - a result that cannot be explained just by luck - we simulate a trading strategy based on the forest output. The resulting trading strategy produces strong performance, certainly much better than a simple buy and hold, even when adjusted for risk.
Trend, Mean-Reversion or Random Walk? A Statistical Analysis of Price Behavior in Major Markets abstract We examine the price behavior of 56 major markets over the last 16 years applying a set of univariate and multivariate robust statistical tests across different time frequencies. Our results can be considered as an augmented true out-of-sample test of all previous research testing for time-series independence. We find no statistically significant evidence that price movements in calendar time scale consistently deviate from randomness. There are only limited departures that are split between trend and mean-reversion depending on the time frame, prices (spot or futures) and assets studied, but even those cases have to be interpreted with caution since we tested a large number of assets increasing the probability of Type I error. Our univariate results are also confirmed by the multivariate analysis that examines the joint distribution of assets using a pooled panel regression. Finally, comparing our results to previous research we find evidence that price dynamics change over time highlighting the difficulty of identifying price patterns ex-ante.
Restructuring Sovereign Debt after NML v. Argentina abstract The decade and a half of litigation that followed Argentina’s sovereign bond default in 2001 ended with a great disturbance in the Force. A new creditor weapon had been uncloaked: The prospect of a court injunction requiring the sovereign borrower to pay those creditors that decline to participate in a debt restructuring ratably with any payments made to those creditors that do provide the country with debt relief. For the first time holdouts succeeded in fashioning a weapon that could be used to injure their erstwhile fellow bondholders, not just the sovereign issuer. Is the availability of this new weapon limited to the aggravated facts of the Argentine default or has it now moved permanently into the creditors’ arsenal? Only time (and future judicial decisions) will tell. In the meantime, however, sovereigns will occasionally find themselves in financial distress and their debts will occasionally need to be restructured. Venezuela already casts this chilly shadow over the sovereign debt market. If, in a galaxy not too far away, sovereign debt workouts are to have any chance of an orderly completion, a method must be found to neutralize this new weapon. Judging by the secondary market prices of different series of Venezuelan sovereign bonds, large amounts of money are being wagered that it cannot be done.
Fast Measurement of Market Volatility Using Ensemble Averaging abstract We propose a number of volatility measures that are based on ensemble averaging instead of time averaging. These measures allow fast measurement of current volatility without relying on series of past data (realized volatility) of future expectations (implied volatility). The introduced quantities are tested on a model market and are then related to actual market data. They display very adequate behavior and are great complement to traditional volatility measures in analytics, securities valuation, risk management and portfolio management.
International Financial Integration Through Equity Markets: Which Firms from Which Countries Go Global? abstract The authors study international financial integration analyzing firms from various countries raising capital, trading equity, and cross-listing in major world stock markets. Using a large sample of 39,517 firms from 111 countries covering the period 1989-2000, they find that, although international financial integration increases substantially over this period, only relatively few countries and firms actively participate in international markets. Firms more likely to internationalize are from larger and more open economies, with higher income, better macroeconomic policies, and worse institutional environments. These firms tend to be larger, grow faster, and have higher returns and more foreign sales. While changes occur with internationalization, these firm attributes are present before internationalization takes place. The results suggest that international financial integration will likely remain constrained by country and firm characteristics.
Regulating Merchants of Liquidity: Market Making from Crowded Floors to High-Frequency Trading abstract This Article develops a framework for analyzing the very existence of regulation of market makers and singles out such key factors as externalities in the market for liquidity, vulnerability of these market participants to certain trading strategies, and their own opportunism. This framework is explored through the evolution of the market making segment of the securities industry from crowded floors to high-frequency trading, and the regulatory outlook is analyzed from the standpoint of the current market structure crisis.
News Versus Sentiment: Predicting Stock Returns from News Stories abstract This paper uses a dataset of more than 900,000 news stories to test whether news can predict stock returns. We measure sentiment with a proprietary Thomson-Reuters neural network. We find that daily news predicts stock returns for only 1 to 2 days, confirming previous research. Weekly news, however, predicts stock returns for one quarter. Positive news stories increase stock returns quickly, but negative stories have a long delayed reaction. Much of the delayed response to news occurs around the subsequent earnings announcement.
Adaptive Time Series Momentum – Benchmark for Trend-Following Funds abstract We propose an objective market-based benchmark for trend-following Managed Futures that relies on naïve time series momentum strategies. Thus, we offer investors an unbiased alternative to widely used manager-based benchmarks. Our benchmark adaptively increases exposure when trends are developing and decreases exposure when trends are fading by allocating to a number of shorter and longer-term momentum strategies at the same time. With respect to existing market-based benchmarks, our model significantly improves the explanatory power of asset pricing equations applied to Managed Futures returns.
Non-GAAP Reporting: A Comparability Crisis abstract The SEC, FASB, and IASB have expressed interest in the recent proliferation of non-GAAP reporting, raising questions about what this increasing reporting trend means for IFRS- and GAAP-based reporting. Our goal is (1) to inform standard setters and regulators about the current state of non-GAAP reporting and (2) explore how the discretion afforded in non-GAAP reporting influences earnings consistency and comparability, two tenets of IFRS- and GAAP-based earnings. We begin by providing an up-to-date discussion of the most common questions examined in the extant literature to provide insights on what academics have learned to date about non-GAAP reporting. Next, we utilize a novel dataset of detailed non-GAAP disclosures to provide in-depth descriptive evidence on the current state of non-GAAP reporting. We find that the frequency of non-GAAP reporting has increased by 35% in recent years, a trend that we find in every sector. We also provide evidence on how the frequency and magnitude of specific exclusions has changed over time. Of particular interest is the increasing frequency with which firms exclude items that are not commonly excluded by other firms, indicating that more idiosyncratic definitions of non-GAAP earnings are emerging in the marketplace. Finally, we examine how discretion in non-GAAP reporting affects earnings consistency and comparability. We find that the consistency with which firms exclude specific items varies across exclusion types. Importantly, we also find some evidence that inconsistent non-GAAP reporting is associated with lower quality non-GAAP metrics. In examining across-firm comparability, we find descriptive evidence indicating that within-sector performance rankings based on GAAP earnings better explains concurrent stock returns than comparisons based on non-GAAP earnings. However, our preliminary analyses indicate that these differences are not statistically significant.