Katten's Lance Zinman on High-Speed Trading (corpcounsel.com)
Citigroup Punished for Treasury Market Spoofing by Five Traders (bloomberg.com)
The SEC's Reaction To High Frequency Trading (seekingalpha.com)
Powerful hedge funds with mediocre performance are charging investors even higher fees than we thought (businessinsider.com)
Move Over, Coders—Physicists Will Soon Rule Silicon Valley (wired.com)
Trending academic research:
See also: Most popular SSRN papers
Local, Stochastic, Local/Stochastic, Volatility Models – And Non-Models abstract We examine local/stochastic volatility models and derive a simple condition such models need to obey so that the carry P&L of a delta-hedged/vega-hedged position makes sense in a trading context.
We give examples of admissible and non-admissible models and discuss the issue of the delta position in the hedge portfolio.
We end with a characterization of the break-even levels of the local volatility model - itself in the admissible class.
Cloaked Trading abstract Using a novel, proprietary database of micro-level trading activities by asset managers, we show strong evidence of asset managers engaging in strategic trading in order to “cloak” their most valuable trades. This takes the form, for instance, of a manager who sells her entire position of Microsoft on March 30, and then repurchases to re-establish the same position on April 1. This manager will economically be holding the same position throughout, yet without having to publicly signal this position. These cloaked trades earn an abnormal return of 370 basis points in the following month, or over 36% per year. We further show that the same managers do not engage in such information-rich cloaked trading around other month ends (non-reporting months), nor in institutional accounts (that are not subject to the reporting requirements) at the exact same quarter-end dates. Moreover, the returns to these cloaked trades continue to accrue over the subsequent quarter and do not reverse in the following year, implying that these cloaked trades are informative about fundamental firm value, which is gradually revealed into prices.
Forecasting ETFs with Machine Learning Algorithms abstract In this work, we apply cutting edge machine learning algorithms to one of the oldest challenges in finance: Predicting returns. For the sake of simplicity, we focus on predicting the direction (e.g. either up or down) of several liquid ETFs and do not attempt to predict the magnitude of price changes. The ETFs we use serve as asset class proxies. We employ approximately five years of historical daily data obtained through Yahoo Finance from January 2011 to January 2016. Utilizing our supervised learning classification algorithms, readily available from Python’s Scikit-Learn, we employ three powerful techniques: (1) Deep Neural Networks, (2) Random Forests, and (3) Support Vector Machines (linear and radial basis function). We document the performance of our three algorithms across our four information sets. We segment our information sets into (A) past returns, (B) past volume, (C) dummies for days/months, and a combination of all three. We introduce our “gain criterion” to aid in our comparison of classifiers’ performance. First, we find that these algorithms work well over the one-month to three-month horizons. Short-horizon predictability, over days, is extremely difficult, thus our results support the short-term random walk hypothesis. Second, we document the importance of cross-sectional and intertemporal volume as a powerful information set. Third, we show that many features are needed for predictability as each feature provides very small contributions. We conclude, therefore, that ETFs can be predicted with machine learning algorithms but practitioners should incorporate prior knowledge of markets and intuition on asset class behavior.
Targeting Market Neutrality abstract Neutralizing portfolios from overall market risk is an important issue in investment management particularly for hedge funds. In this paper we show an economically significant improvement in the accuracy of targeting market neutrality for equity portfolios. Key features of the approach are the relatively short forecast horizon of one week and forecasting with realized beta estimators computed using high frequency data provided by QuantQuote. We also find that too long and too short estimation windows result in poor beta forecasts and that the optimal length of estimation window depends on the frequency of return observations.
Fake Alpha abstract Why do investors entrust active mutual fund managers with large sums of money while receiving negative excess returns on average? Our explanation is that investors have a coarser information set than fund managers which leads them to systematically misinterpret managers' skill. When investors are unable to correctly quantify risk because they have no knowledge of factor investing on beyond-market-risk factors, Fake Alpha strategies based on factor investing look like skill from the investors' perspective. As running such strategies is relatively cheap for the managers, the investors' coarser information set misleads them to invest beyond the point of zero excess returns in equilibrium. We confirm our theory by analyzing the sample of US equity active managed mutual funds and find significant evidence of decreasing returns to scale at the fund level as well as negative excess returns to investors in equilibrium states.
Exchange Rates and the Yield Curve abstract In this paper, we confront the data with the financial markets folk wisdom that an increase in a yield or forward rate of country i relative to j is associated with a contemporaneous appreciation of currency i. We find that while the folk wisdom prior to 2009:Q1 holds fairly well for all maturities and three major currency bases, the “coefficient curve” twisted during the zero-lower-bound period so that the relationship became stronger at the short end but weaker and even of the opposite sign at the long end of the curve. We attribute the structural breaks at the short end of the curve to a change in the relationship between expected excess currency returns and changes in relative yields/forwards. The breaks at the long end of the curve can be explained by changing relationships between yields/forwards and the part of exchange rate fluctuations due to changes in expectations over future short-term rates and long-run relative price levels. Alternatively, the twist of the coefficient curve can be attributed to the changing relationship between the exchange rate and the expectation hypothesis component of yields/forwards at the short end and the term premium component at the long end.
Applying a Systematic Investment Process to Distributive Portfolios: A 150 Year Study Demonstrating Enhanced Outcomes Through Trend Following abstract The objective of this paper is to examine the absolute and risk-adjusted effects on distribution rates and total wealth created by adding loss-limiting trend following strategies to buy and hold portfolios. Using 150 years of equity and bond data, we found that applying trend following to distributive portfolios results in less frequent and less severe failures compared with a buy and hold strategy. Additionally, we concluded that trend following allows for an increased allocation to equities without increased volatility relative to buy and hold. As a result, at the same level of volatility, portfolios can create greater total wealth and distributions.
The Growth of Relative Wealth and the Kelly Criterion abstract We propose an evolutionary framework for optimal portfolio growth theory in which investors allocate their wealth between two assets. By considering both absolute wealth and relative wealth between investors, we show that different investor behaviors survive under different environments. When investors maximize their relative wealth, the Kelly criterion is optimal only under certain identified conditions. It is shown that the initial relative wealth plays a critical role in determining the deviation of optimal behavior from the Kelly criterion, whether the investor is myopic across a single time period or is maximizing infinite-horizon wealth.
Who Provides Liquidity and When: An Analysis of Price vs. Speed Competition on Liquidity and Welfare abstract I model the interaction between buy-side algorithmic traders (BATs) and high-frequency traders (HFTs). When the minimum price variation (tick size) is small, BATs dominate liquidity provision by establishing price priority over HFTs in the limit order book (LOB), because providing liquidity is less costly than demanding liquidity from HFTs. A large tick size, however, constrains price competition and encourages HFTs to provide liquidity by establishing time priority. An increase in adverse selection risk raises the unconstrained bid-ask spread, reduces tick size constraints, and discourages HFTs’ liquidity provision. An increase in tick size increases transaction costs and harms liquidity demanders, but it does not benefit liquidity providers because the costs of speed investments dissipate the rents resulting from the tick size. I predict that mini-flash crashes are more likely to occur for stocks with a smaller tick size and higher adverse selection risk. I suggest that the literature should not use the message-to-trade ratio as a cross-sectional proxy for HFTs’ liquidity provision because stocks with more liquidity provided by HFTs have a lower message-to-trade ratio.
Implied Volatility Sentiment: A Tale of Two Tails abstract Low probability events are overweighted in the pricing of out-of-the-money index puts and single stock calls. This behavioral bias is strongly time-varying, and is linked to equity market sentiment and higher moments of the risk-neutral density. We find that our implied volatility (IV) sentiment measure, jointly derived from index and single stock options, explains investors’ overweight of tail events well. When employed within a trading strategy, our IV-sentiment measure delivers economically significant results, which are more consistent than the ones produced by the market sentiment factor. Out-of-sample tests on reversal prediction show that our IV-sentiment measure adds value over and above traditional factors in the equity risk premium literature.
Research Note: The Economic Benefit of Forecasting Market Components for Mean-Variance Investors abstract Existing studies focus on variables’ predictive quality with respect to the aggregated stock market, which per definition contains a minimum level of idiosyncratic risk and provides a favorable environment for such applications. Economic intuition suggests that the level of out-of-sample predictability decreases as we climb down the ladder from market aggregates to industries and ultimately single stock returns. Thereon, we ask the central question:
Do forecasting errors from direct predictions of market components out-way the additional errors introduced by an intermediary asset pricing model?
This is an early stage research note and we welcome any feedback and comments.
The Mythology of Rebalancing: A Random Walk down Performance and Risk Management abstract dvisors and target date funds. One of the appealing aspects of these platforms and products is that they delegate all investment decisions to the asset manager (who is believed to be more sophisticated) and away from the asset owner (who is typically unsophisticated). The appeal of these products and platforms is driven by their ability to make effective forecasts of assets, derive an effective asset allocation, and then to rebalance the portfolios on a periodic basis to this target asset allocation. In this paper, we will just focus on the robo-advisors and argue that one key activity underlying these platforms and even practiced very broadly in institutional investing and defined contribution funds, naïve rebalancing, is predicated on bad theory and myths and fails the key test of whether it truly improves performance and/or risk management. We will demonstrate that many previous studies of rebalancing examine this issue from a simplistic perspective of a two-asset portfolio and a simple measure of risk, volatility. When we examine these strategies from the perspective of a multi-asset portfolio and a broader set of risk statistics (that a sophisticated investor would apply), then these naïve rebalancing strategies are nothing more than a form of poor market timing and the performance is a coin-toss and the risk profile of the portfolio is typically worsened. Once these flaws of naïve rebalancing are exposed, we suggest that investors would be well advised to adopt a more intelligent form of rebalancing, where an intelligent analysis is conducted of the relative attractiveness of assets to then set allocations within a client’s policy ranges. This approach has a higher likelihood of improving performance and risk management and the ideas to implement such a program are in the public domain.
Predictability and Mispricing in Emerging vs. Developed Currency Markets abstract In this paper, we study predictability of returns on currencies of emerging and developed economies over the period February 1994 - July 2016. To assess the economic significance of currency predictability, we construct an upper bound on the explanatory power of predictive regressions, motivated by “no good-deal” restrictions that rule out unduly attractive investment opportunities. This bound is an extension of the bound proposed by Ross (2005) in that it allows for imperfect correlation between the kernel and trading strategies that exploit predictability. We find some evidence of predictability of futures returns outside of this bound, for realistic levels of risk aversion, for the emerging markets in our sample. We find, however, no excess-predictability of excessreturns computed from spot exchange rates and spot interest rates, in contrast with the results reported by Hsu et al. (2016) but in agreement with Kuang et al. (2014). The different predictability of spot and futures excess-returns is a noteworthy finding with potentially deep implications for the price discovery mechanism in currency markets. In addition, we find that emerging markets reacted differently from developed market, in terms of informational efficiency, to the onset of the ’post-Lehman’ financial crisis.
Decomposing the Size Premium abstract We decompose firm size into four components: the lagged 5-year component that represents size five years ago, and the long-run, intermediate-run, and short-run components that capture changes in size in each horizon. Our analyses indicate that while the lagged 5-year component explains about 80% of the cross-sectional variation in size, it has little return predictability. In contrast, the long-run change in size component explains only 18% of size, but it completely captures the size premium. Our decomposition also sheds light on the January effect, the disappearance of the size premium since early 1980s, and the return behaviors of new entrants.
No Matter the Winning Presidential Candidate, 'Buying at Halloween and Selling in May' Has Been Attractive for Equities in Pre-Election Years, with the Opposite for Treasuries abstract This study shows that since 1927, investors would have earned a statistically significant excess return of nearly two percent per month by investing in the U.S. equity market from November through April in presidential pre-election years. On the other hand, Treasury bond returns performed inversely to the equity returns, i.e., they have been higher in summer (May to October) months and in other-than-pre-election-years (especially in midterm election years). Our equity results suggest that the previously documented Halloween and pre-election year effects are intertwined. The combined Halloween–pre-election year effect shows up consistently in sub-periods; in an extended sample period since 1871; and in international stock markets. It appears to be separate from a January anomaly; it is independent of the political party in the White House; and it doesn’t appear to be a compensation for higher risk. In contrast, small (value) stocks outperform large (growth) stocks in the November–to–April period in years other than presidential pre-election years. We show that the winter–pre-election year premiums align with the Baker et al. (2016) measure of economic policy uncertainty, and we propose that models of political uncertainty potentially explain both the equity and bond results.
Higher-Order Tail Moments in Asset-Pricing Theory abstract We review the literature about the use of third- and fourth-order moments in finance, the main papers on asset pricing theory with higher-order moments, and the definitions of skewness and kurtosis in the statistical literature. Contagion, skewness and kurtosis investor preferences and tail regimes are some of the topics discussed in this paper. We derived theoretical results about higher-order moments of the bivariate truncated normal distribution, and analyzed the implications of the results for previous empirical tests. We provide these results as a tool to be used in empirical testing of asymmetries and heavy tailedness of assets returns.
Who Provides Liquidity, and When: An Analysis of Price vs Speed Competition on Liquidity and Welfare abstract I model the interaction between buy-side algorithmic traders (BATs) and high-frequency traders (HFTs). When the minimum price variation (tick size) is small, BATs dominate liquidity provision by establishing price priority over HFTs in the limit order book (LOB), because providing liquidity is less costly than demanding liquidity from HFTs. A large tick size, however, constrains price competition and encourages HFTs to provide liquidity by establishing time priority. An increase in adverse selection risk raises the unconstrained bid-ask spread, reduces tick size constraints, and discourages HFTs’ liquidity provision. An increase in tick size increases transaction costs and harms liquidity demanders, but it does not benefit liquidity providers because the costs of speed investments dissipate the rents resulting from the tick size. I predict that mini-flash crashes are more likely to occur for stocks with a smaller tick size and higher adverse selection risk. I suggest that the literature should not use the message-to-trade ratio as a cross-sectional proxy for HFTs’ liquidity provision because stocks with more liquidity provided by HFTs have a lower message-to-trade ratio.
Regularized Decomposition Methods for Deterministic and Stochastic Convex Optimization and Application to Portfolio Selection with Direct Transaction and Market Impact Costs abstract We define a regularized variant of the Dual Dynamic Programming algorithm called REDDP (REgularized Dual Dynamic Programming) to solve nonlinear dynamic programming equations. We extend the algorithm to solve nonlinear stochastic dynamic programming equations. The corresponding algorithm, called SDDP-REG, can be seen as an extension of a regularization of the Stochastic Dual Dynamic Programming (SDDP) algorithm recently introduced which was studied for linear problems only and with less general prox-centers. We show the convergence of REDDP and SDDP-REG. We assess the performance of REDDP and SDDP-REG on portfolio models with direct transaction and market impact costs. In particular, we propose a risk-neutral portfolio selection model which can be cast as a multistage stochastic second-order cone program. The formulation is motivated by the impact of market impact costs on large portfolio rebalancing operations. Numerical simulations show that REDDP is much quicker than DDP on all problem instances considered (up to 184 times quicker than DDP) and that SDDP-REG is quicker on the instances of portfolio selection problems with market impact costs tested and much faster on the instance of risk-neutral multistage stochastic linear program implemented (8.2 times faster).
The Wisdom of Large and Small Crowds: Evidence from Repeated Natural Experiments in Sports Betting abstract Prediction markets have proved excellent tools for forecasting, outperforming experts and polls in many settings. But do larger markets, with wider participation, perform better than smaller markets? In this paper we analyse a series of repeated natural experiments in sports betting. The Queen's Club Tennis Championships are held every year, but every other year the Championships clash with a major soccer tournament. We find that tennis betting prices become significantly less informative when participation rates are adversely affected by the clashing soccer tournament. Larger markets perform better, in part, because of the higher returns they offer for informed trading.
Some correspondences between Index Number Theory in economy and the General Theory of Relativity in physics abstract GDP of China is about 11 trillion dollars and GDP of the United States is about 18 trillion dollars. Suppose that we know for the coming years, economy of the US will experience a real growth rate equal to \%3 and economy of China will experience a real growth as of \%6. Now, the question is how long does it take for economy of China to catch the economy of the United States. The early impression is that the desired time is the answer of the equation $11\times1.06^X=18\times1.03^X$. The correct answer however is quite different. GDP is not a simple number and the gap between two countries can not be addressed simply through their sizes. It is rather a geometrical object. Countries pass different paths in the space of production. The gaps between GDP of different countries depend on the path that each country passes through and local metric. To address distance between economies of China and of the US we need to know their utility preferences and the path that China passes to reach the US size. The true gap then can be found if we calculate local metric along this path. It resembles impressions about measurements in the General Theory of Relativity. Path dependency of aggregate indexes is widely discussed in the Index Number Theory. Our aim is to stick to the geometrical view presented in the General Relativity to provide a visual understanding of the matter. We show that different elements in the general relativity have their own counterparts in economics. We claim that national agencies who provide aggregate data resemble falling observers into a curved space time. It is while the World Bank or international organizations are outside observers. The vision provided here, leaves readers with a clear conclusion. If China keeps its growth rate, then the economy of China should catch the economy of the United States sooner than what we expect.
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