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Trending academic research:
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Beyond Sorting: A More Powerful Test for Cross-Sectional Anomalies abstract Many researchers seek factors that predict the cross-section of stock returns. The standard methodology sorts stocks according to their factor scores into quantiles and forms a corresponding long-short portfolio. Such a course of action ignores any information on the covariance matrix of stock returns. Historically, it has been difficult to estimate the covariance matrix for a large universe of stocks. We demonstrate that using the recent DCC-NL estimator of Engle et al. (2016) substantially enhances the power of tests for cross-sectional anomalies: On average, ‘Student’ t-statistics more than double.
Man vs. Machine: Comparing Discretionary and Systematic Hedge Fund Performance abstract We analyse and contrast the performance of discretionary and systematic hedge funds. Systematic funds use strategies that are rules-based, with little or no daily intervention by humans. In our experience, some large allocators shy away from systematic hedge funds altogether. A possible explanation for this is what the psychology literature calls “algorithm aversion”. We find that, for the period 1996-2014, systematic equity managers underperform their discretionary counterparts in terms of unadjusted (raw) returns, but that after adjusting for exposures to well-known risk factors, the risk-adjusted performance is similar. In the case of macro, systematic funds outperform discretionary funds, both on an unadjusted and risk-adjusted basis. It is sometimes claimed that systematic funds’ returns have a greater exposure to well-known risk factors. We find, however, that for discretionary funds (in the aggregate) more of the average return and the volatility of returns can be explained by risk factors.
Evaluating the Performance of ANN Prediction System at Shanghai Stock Market in the Period 21-Sep-2016 to 11-Oct-2016 abstract This research evaluates the performance of an Artificial Neural Network based prediction system that was employed on the Shanghai Stock Exchange for the period 21-Sep-2016 to 11-Oct-2016. It is a follow-up to a previous paper in which the prices were predicted and published before September 21. Stock market price prediction remains an important quest for investors and researchers. This research used an Artificial Intelligence system, being an Artificial Neural Network that is feedforward multi-layer perceptron with error backpropagation for prediction, unlike other methods such as technical, fundamental or time series analysis. While these alternative methods tend to guide on trends and not the exact likely prices, neural networks on the other hand have the ability to predict the real value prices, as was done on this research. Nonetheless, determination of suitable network parameters remains a challenge in neural network design, with this research settling on a configuration of 5:21:21:1 with 80% training data or 4-year of training data as a good enough model for stock prediction, as already determined in a previous research by the author. The comparative results indicate that neural network can predict typical stock market prices with mean absolute percentage errors that are as low as 1.95% over the ten prediction instances that was studied in this research.
Analyst Forecast Momentum abstract A great number of academic papers criticize incentive-driven bias in sell-side analysts' earnings forecasts. Yet bias does not necessarily invalidate a forecast, nor does it impinge on its relative quality. We find that analysts' forecasts are optimistic relative to recently introduced fundamental alternatives. However, analysts' forecasts have lower absolute deviation and the information in their earnings forecasts has predictive value for near-term stock returns. We propose the latter result as a previously unidentified form of earnings momentum. We suggest that analysts' strongest incentives are directed to forecasting quarterly earnings, rather than annual earnings. As a result, investing with optimistic analysts is a rational investment strategy, rather than a misguided one, when the investment horizon is shorter than one year.
Macroeconomic 'Action-at-a-Distance', Economic Fields and Surface Waves abstract This paper describes new macroeconomic wave processes on economic space. We model macroeconomics as multi-agent system and risk ratings of economic agents play role of their coordinates on economic space. Number n of risks for which ratings of economic agents are measured defines dimension n of economic space. Aggregates of economic or financial variables of economic agents near point x define macroeconomic variables as functions of coordinates on economic space. Evolution of macroeconomic variables like Investment, GDP, Demand, Credits and etc., is determined by economic and financial transactions between economic agents. Transactions between economic agents at points x and y describe macroeconomic “action-at-a-distance” on economic space. Aggregates of transactions between economic agents at point x and y determine economic fields A(x,y) as functions of two variables on economic space. Such economic fields A(x,y) can describe amount of Credits provided from economic agents at point x to economic agents at point y and thus amount of Loans received by agents at point y from agents at point x. Economic fields determine evolution of all macroeconomic variables on economic space. We describe economic fields by hydrodynamic-like equations on economic space with 2n-dimension. Economic agents fill macroeconomic area that is determined by minimum and maximum risk grades that define most risky and most secure economic agents. Economic and financial shocks can disturb steady borders of macroeconomic area and cause perturbations of economic fields values. Disturbances of economic fields can generate waves that propagate along macroeconomic borders alike to surface waves in fluids. For simplest example that models one-dimensional economic space and interaction between two economic fields that describe Commodities Supply and Payments for Commodities we derive hydrodynamic like equations on economic fields in a closed form. For this model we derive economic field surface wave equations. Diversity of waves in simplest macroeconomic models on economic space designates importance of concealed wave processes for macroeconomic modeling and crises forecasting.
Implied Volatility Duration and the Early Resolution Premium abstract We introduce Implied Volatility Duration (IVD) as a new measure for the timing of the resolution of uncertainty about a stock's cash flows. The shorter the IVD, the earlier the resolution of uncertainty. Portfolio sorts indicate that investors demand on average about seven percent return per year in exchange for a late resolution of uncertainty, and this premium cannot be explained by standard factor models. We find that the premium is higher in times of increased economic uncertainty and low market returns. We show in a general equilibrium model that the expected excess returns on high IVD stocks exceed those of low IVD stocks if and only if the investor's relative risk aversion exceeds the inverse of her elasticity of intertemporal substitution, i.e., if she exhibits a 'preference for early resolution of uncertainty' in the spirit of Epstein and Zin (1989). Our empirical analysis thus provides a purely market-based assessment of the relation between two preference parameters, which are usually hard to estimate.
Advanced EONIA Curve Calibration abstract This work analyzes and proposes solutions for subtle, but relevant, problems related to the EONIA curve calibration. The first issue examined is how to deal with jumps and turn-of-year effects. The second point is related to the problem caused by imperfect concatenation between spot starting OIS and forward starting ECB dated OIS: in order to avoid distortion, a meta-instrument called "Forward Stub" should cover the section between the maturity of the last spot starting OIS and the settlement of the first ECB OIS. Its implied value can be derived assuming a no-arbitrage conditions. The final issue is the empirical evidence that the forward overnight rates are generally constant between ECB monetary policy board meeting dates: because of this, a log-linear discount interpolation is a good fit. Anyway, flat forward rates are hardly realistic on the long end. This is the rationale to suggest the use of a "Mixed Interpolation" which merges two different interpolation regimes. All the algorithms used to perform the analysis are implemented in the open-source QuantLib project.
Order statistics of horse racing and the randomly broken stick abstract We find a remarkable agreement between the statistics of a randomly divided interval and the observed statistical patterns and distributions found in horse racing betting markets. We compare the distribution of implied winning odds, the average true winning probabilities, the implied odds conditional on a win, and the average implied odds of the winning horse with the corresponding quantities from the "randomly broken stick problem". We observe that the market is at least to some degree informationally efficient. From the mapping between exponential random variables and the statistics of the random division we conclude that horses' true winning abilities are exponentially distributed.
Expected Investment Growth and the Cross Section of Stock Returns abstract Expected investment growth (EIG) is a strong predictor for cross-sectional stock returns. Between July 1953 and December 2015 in the US, an investment strategy that takes a long position in firms with high EIG and a short position in firms with low EIG generates an average annual return of more than 20%, with a Sharpe ratio of 1.01. This return predictability holds both in subperiods and in different subsamples of firms, as well as in all other G7 countries. Leading empirical factor models including CAPM, Fama-French three-factor model, Carhart four-factor model, and the recent Hou, Xue, and Zhang four-factor model and Fama and French five-factor model all fail to fully capture the profitability of this investment strategy. Further analyses suggest that EIG is closely related to financial distress risk, especially at a short horizon up to one year, and is a better predictor of stock returns than failure probability from Campbell, Hilscher, and Szilagyi (2008). We provide supporting evidence for both risk-based explanation and behavioral explanation for this large EIG premium.
Wavelet-based methods for high-frequency lead-lag analysis abstract We propose a novel framework to investigate lead-lag relationships between two financial assets. Our framework bridges a gap between continuous-time modeling based on Brownian motion and the existing wavelet methods for lead-lag analysis based on discrete-time models and enables us to analyze the multi-scale structure of lead-lag effects. We also present a statistical methodology for the scale-by-scale analysis of lead-lag effects in the proposed framework and develop an asymptotic theory applicable to a situation including stochastic volatilities and irregular sampling. Finally, we report several numerical experiments to demonstrate how our framework works in practice.
Who Benefits in a Crisis? Evidence from Hedge Fund Stock and Option Holdings abstract We use a unique data set of hedge fund long equity and equity option positions to investigate a significant lockup-related premium that is earned during the Tech Bubble and Financial Crisis. Net fund flows are significantly greater among lockup funds during both crisis and non-crisis periods. Managers of hedge funds with locked up capital trade opportunistically against flow-motivated trades of nonlockup managers, consistent with a hypothesis of rent extraction in provision of crisis era liquidity. Moreover, the success of this opportunistic trading is concentrated in less liquid stock markets and enhanced by hedging in the equity option market.
Basic Commodities and Multiple Interest Rate Analysis abstract This paper considers the application of multiple interest rate analysis to a model of the production of commodities by means of commodities. A polynomial, for the characteristic equation of the augmented input-output matrix, is used in defining the rate of profits in such a model. Only one root is found to be economically meaningful. No non-trivial application of multiple interest rate analysis is found in the analysis of the choice of technique. On the other hand, multiple interest rate analysis can be used in defining Net Present Value in an approximate model, in which techniques are represented as finite series of dated labor inputs. The product of the quantity of the first labor input and the composite interest rate approaches, in the limit, the difference between the labor commanded by and the labor embodied in final output in the full model.
The Information Value of Stock Lending Fees: Are Lenders Price Takers? abstract We find that higher stock lending fees predict significantly lower future returns after controlling for shorting demand for U.S. stocks during the period 2007–2010. These results suggest that active institutional investors on the supply side play an important role in the return predictability of fees and they not only respond to demand but also price in additional information around earnings news announcements. Overall, we find evidence that stock lenders are informed and, together with short sellers, contribute to the price discovery process.
Pricing within and across Asset Classes abstract When an asset-pricing model is claimed to explain a cross-section of portfolio returns, it should do so both within one asset class and across different asset classes. This note illustrates that this is not always the case by testing the explanatory power of the CAPM and the Asness, Moskowitz and Pedensen (2013) model for momentum and value portfolios in different asset markets. Apparently, on one hand, the CAPM is almost as good and the AMP model in explaining momentum and value returns across asset classes, but on the other hand, the AMP model is almost as bad as the CAPM in explaining momentum and value returns within one asset class, namely US equities. Therefore, applying an asset-pricing model to a single cross-section of returns may generate misleading results.
Prepayment Risk and Expected MBS Returns abstract We present a simple, linear asset pricing model of the cross section of Mortgage-Backed Security (MBS) returns. We measure prepayment risk and estimate security risk loadings using real data on prepayment forecasts vs. realizations. Estimated loadings are monotonic in securities’ coupons relative to the par coupon, as predicted by the model. Prepayment risks appear to be priced by specialized MBS investors. In particular, we find convincing evidence that prepayment risk prices change sign over time with the sign of a representative MBS investor’s exposure to prepayment risk.
Overseas Unspanned Factors and Domestic Bond Returns abstract Using data on government bond yields in Germany and the United States, we show that overseas unspanned factors — constructed from the components of overseas yields that are uncorrelated with domestic yields — have significant explanatory power for subsequent domestic bond returns. This result is remarkably robust, holding for different sample periods, as well as out of sample. Shocks to overseas unspanned factors have large and persistent effects on domestic yield curves. Dynamic term structure models that omit information about foreign bond yields are therefore likely to be misspecified.
Hedging with Temporary Price Impact abstract We consider the problem of hedging a European contingent claim in a Bachelier model with transient price impact as proposed by Almgren and Chriss . Following the approach of Rogers and Singh  and Naujokat and Westray , the hedging problem can be regarded as a cost optimal tracking problem of the frictionless hedging strategy. We solve this problem explicitly for general predictable target hedging strategies. It turns out that, rather than towards the current target position, the optimal policy trades towards a weighted average of expected future target positions. This generalizes an observation of Gârleanu and Pedersen  from their homogenous Markovian optimal investment problem to a general hedging problem. Our findings complement a number of previous studies in the literature on optimal strategies in illiquid markets as, e.g., , , , , , , , , where the frictionless hedging strategy is confined to diffusions. The consideration of general predictable reference strategies is made possible by the use of a convex analysis approach instead of the more common dynamic programming methods.
How many market makers does a market need? abstract We consider a simple model for the evolution of a limit order book in which limit orders of unit size arrive according to independent Poisson processes. The frequency of buy limit orders below a given price level, respectively sell limit orders above a given level are described by fixed demand and supply functions. Buy (resp. sell) limit orders that arrive above (resp. below) the current ask (resp. bid) price are converted into market orders. There is no cancellation of limit orders. This model has independently been reinvented by several authors, including Stigler in 1964 and Luckock in 2003, who was able to calculate the equilibrium distribution of the bid and ask prices. We extend the model by introducing market makers that simultaneously place both a buy and sell limit order at the current bid and ask price. We show how the introduction of market makers reduces the spread, which in the original model is unrealistically large. In particular, we are able to calculate the exact rate of market makers needed to close the spread completely.
Multi-Purpose Binomial Model: Fitting all Moments to the Underlying Geometric Brownian Motion abstract We construct a binomial tree model fitting all moments to the approximated geometric Brownian motion. Our construction generalizes the classical Cox-Ross-Rubinstein, the Jarrow-Rudd, and the Tian binomial tree models. The new binomial model is used to resolve a discontinuity problem in option pricing.
Ownership Structure, Incentives, and Asset Prices abstract We develop a dynamic equilibrium model to study the interplay among managerial incentive contracts, large shareholders' ownership dynamics, and asset prices. Our unified framework integrates an asset pricing model with a dynamic principal-agent model by distinguishing a firm's large shareholders from small shareholders. Large shareholders play the role of mediators who determine managerial incentive contracts, while also influencing asset prices through their dynamic trading decisions. Large shareholders' ownership dynamics, managerial contracts, and asset prices are endogenously determined in equilibrium. Relative to the benchmark owner-manager case, agency conflicts between large shareholders and managers lead to more volatile and higher expected stock returns. Risk sharing between large shareholders and managers makes block ownership dynamics effectively insulated from fluctuations in firm-specific parameters. We derive a number of novel empirical implications for the equilibrium relations among block ownership levels, managerial incentives, and stock return characteristics. Our results suggest that block ownership and incentive contracts serve as complementary corporate governance mechanisms in influencing stock returns.
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