Forecasting Stock Returns: New Out-of-Sample Evidence

2000 ◽  
Author(s):  
Martin Lettau ◽  
Sydney C. Ludvigson
Keyword(s):  
Stock Returns ◽  
Out Of Sample

Investor Sentiment and Stock Returns

2015 ◽  
pp. 315-328
Author(s):  
Serkan Yılmaz Kandır ◽  
Veli Akel ◽  
Murat Çetin
Keyword(s):  
Stock Returns ◽  
Investor Sentiment ◽  
Consumer Confidence ◽  
Cointegration Test ◽  
Sample Period ◽  
Monthly Data ◽  
Out Of Sample ◽  
Confidence Index ◽  
The Relationship

In this chapter, the authors investigate the relationship between investor sentiment and stock returns in an out of sample market, namely Borsa Istanbul. The authors use the Consumer Confidence Index as an investor sentiment proxy, while utilizing BIST Second National Index as a measure of small capitalized stock returns. The sample period spans from January 2004 to May 2014. By using monthly data, the authors employ cointegration test and error–correction based Granger causality models. The authors' findings suggest that there is a long-term relationship between investor sentiment and stock returns in Borsa Istanbul. Moreover, a unidirectional causal relationship from investor sentiment to stock returns is also found.

scholarly journals Break Risk

2020 ◽  
Author(s):  
Simon C Smith ◽  
Allan Timmermann
Keyword(s):  
Stock Returns ◽  
Cross Section ◽  
Prediction Models ◽  
Return Predictability ◽  
Present Value ◽  
New Approach ◽  
Present Value Model ◽  
Out Of Sample ◽  
Rate Processes ◽  
Value Model

Abstract We develop a new approach to modeling and predicting stock returns in the presence of breaks that simultaneously affect a large cross-section of stocks. Exploiting information in the cross-section enables us to detect breaks in return prediction models with little delay and to generate out-of-sample return forecasts that are significantly more accurate than those from existing approaches. To identify the economic sources of breaks, we explore the asset pricing restrictions implied by a present value model which links breaks in return predictability to breaks in the cash flow growth and discount rate processes.

Modeling the Cross Section of Stock Returns: A Model Pooling Approach

2012 ◽  
Vol 47 (6) ◽  
pp. 1331-1360 ◽  
Author(s):  
Michael O’Doherty ◽  
N. E. Savin ◽  
Ashish Tiwari
Keyword(s):  
Stock Returns ◽  
Cross Section ◽  
Asset Allocation ◽  
Economic Stress ◽  
Predictive Score ◽  
Asset Pricing Model ◽  
Out Of Sample ◽  
Competing Models ◽  
The Cross ◽  
Allocation Decisions

AbstractModel selection (i.e., the choice of an asset pricing model to the exclusion of competing models) is an inherently misguided strategy when the true model is unavailable to the researcher. This paper illustrates the advantages of a model pooling approach in characterizing the cross section of stock returns. The optimal pool combines models using the log predictive score criterion, a measure of the out-of-sample performance of each model, and consistently outperforms the best individual model. The benefits to model pooling are most pronounced during periods of economic stress, and it is a valuable tool for asset allocation decisions.

scholarly journals Estimating dynamic copula dependence using intraday data

2015 ◽  
Vol 19 (4) ◽  
Author(s):  
Lidan Grossmass ◽  
Ser-Huang Poon
Keyword(s):  
Stock Returns ◽  
Long Memory ◽  
High Frequency ◽  
Time Varying ◽  
Credit Crisis ◽  
Dynamic Copula ◽  
Tick Data ◽  
Out Of Sample ◽  
Intraday Data ◽  
Better Than

AbstractWe estimate the dynamic daily dependence between assets by applying the Semiparametric Copula-Based Multivariate Dynamic (SCOMDY) model on intraday data. Using tick data of three stock returns of the period before and during the credit crisis, we find that our dependence estimator better captures the steep increase in dependence during the onset of the crisis as compared to other commonly used time-varying copula methods. Like other high-frequency estimators, we find that the dependence estimator exhibits long memory and forecast it using a HAR model. We show that for out-of-sample forecasts, our dependence estimator performs better than the constant estimator and other commonly used time-varying copula dependence estimators.

scholarly journals The Evolving Efficiency Of The South African Stock Exchange

2012 ◽  
Vol 11 (9) ◽  
pp. 997 ◽  
Author(s):  
Lumengo Bonga-Bonga
Keyword(s):  
Stock Returns ◽  
South African ◽  
Stock Exchange ◽  
Weak Form ◽  
Time Varying ◽  
The South ◽  
Forecast Performance ◽  
Nested Models ◽  
Out Of Sample ◽  
Weak Form Efficiency

This paper tests the weak-form efficiency in the South African stock exchange - the Johannesburg Securities Exchange (JSE) - under the hypothesis that emerging markets efficiency evolves through time as these markets constantly enhance their regulatory environment. The paper makes use of the time varying GARCH model in testing this hypothesis. In addition, the paper compares the out-of-sample forecast performance of the time varying and fixed parameter GARCH models in predicting stock returns in the JSE making use of MSE-F statistics for nested models proposed (McCracken, 1999). The findings of the paper show that the two models provide the same conclusion in showing that the JSE has been efficient during the period of the analysis. In addition, the time varying model outperforms the fixed coefficient model in predicting the JSE stock returns. This finding indicates that the time-varying parameter model adds a benefit in testing the weak-form efficiency or modelling stock return in the JSE.

Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?

CFA Digest ◽  
2009 ◽  
Vol 39 (1) ◽  
pp. 78-80
Author(s):  
Brendan F. O’Connell
Keyword(s):  
Stock Returns ◽  
Out Of Sample ◽  
Excess Stock Returns

scholarly journals Impact of Economic Policy Uncertainty on the Distribution of China’s Stock Returns: An External Perspective

2019 ◽  
Vol 23 (4) ◽  
pp. 667-677
Author(s):  
Yanyun Yao ◽  
Haijing Yu ◽  
Huimin Wang ◽  
Tsung-Kuo Tien-Liu ◽  
◽  
...  
Keyword(s):  
Economic Policy ◽  
Stock Returns ◽  
Policy Uncertainty ◽  
Return Distribution ◽  
Full Sample ◽  
Economic Policy Uncertainty ◽  
Out Of Sample ◽  
The Mean ◽  
The Impact ◽  
External Uncertainty

This study examines the impact of external economic policy uncertainty on the distribution of China’s stock returns. The Chinese Economic Policy Uncertainty (CEPU) and global EPU (GEPU) indexes compiled by [1] are employed as a measurement of the external uncertainty. An empirical study is conducted using the GARCH-MIDAS framework. The first innovation of this study is extending the symmetric GARCH-MIDAS model to the case of GJR; the leverage effect is therefore considered. The second innovation is considering the impact of EPU on the overall distribution of returns, rather than on the mean or volatility. Full-sample fitting shows that CEPU can explain around 14% of the return volatility, and CEPU together with GEPU can explain about 17%. Out-of-sample recursive forecasting demonstrates that it is meaningful to extend the models to GJR; the EPU information improves the return distribution forecasting. However, the impact of EPUs is limited, which implies that external uncertainty is quite different from the “internal” economic policy uncertainty directly driving the China’s stock market.

scholarly journals A New Kernel of Support Vector Regression for Forecasting High-Frequency Stock Returns

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Hui Qu ◽  
Yu Zhang
Keyword(s):  
Support Vector Regression ◽  
Stock Returns ◽  
High Frequency ◽  
Forecast Accuracy ◽  
Support Vector ◽  
Nonlinear Relationship ◽  
Sigmoid Function ◽  
Accuracy Rate ◽  
Out Of Sample ◽  
Future Return

This paper investigates the value of designing a new kernel of support vector regression for the application of forecasting high-frequency stock returns. Under the assumption that each return is an event that triggers momentum and reversal periodically, we decompose each future return into a collection of decaying cosine waves that are functions of past returns. Under realistic assumptions, we reach an analytical expression of the nonlinear relationship between past and future returns and introduce a new kernel for forecasting future returns accordingly. Using high-frequency prices of Chinese CSI 300 index from January 4, 2010, to March 3, 2014, as empirical data, we have the following observations: (1) the new kernel significantly beats the radial basis function kernel and the sigmoid function kernel out-of-sample in both the prediction mean square error and the directional forecast accuracy rate. (2) Besides, the capital gain of a simple trading strategy based on the out-of-sample predictions with the new kernel is also significantly higher. Therefore, we conclude that it is statistically and economically valuable to design a new kernel of support vector regression for forecasting high-frequency stock returns.

scholarly journals Dispersion Trading Based on the Explanatory Power of S&P 500 Stock Returns

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1627
Author(s):  
Lucas Schneider ◽  
Johannes Stübinger
Keyword(s):  
Stock Returns ◽  
Large Scale ◽  
Selection Process ◽  
Explanatory Power ◽  
Principal Component ◽  
Deep Dive ◽  
Trading Systems ◽  
Dispersion Trading ◽  
Out Of Sample ◽  
The Individual

This paper develops a dispersion trading strategy based on a statistical index subsetting procedure and applies it to the S&P 500 constituents from January 2000 to December 2017. In particular, our selection process determines appropriate subset weights by exploiting a principal component analysis to specify the individual index explanatory power of each stock. In the following out-of-sample trading period, we trade the most suitable stocks using a hedged and unhedged approach. Within the large-scale back-testing study, the trading frameworks achieve statistically and economically significant returns of 14.52 and 26.51 percent p.a. after transaction costs, as well as a Sharpe ratio of 0.40 and 0.34, respectively. Furthermore, the trading performance is robust across varying market conditions. By benchmarking our strategies against a naive subsetting scheme and a buy-and-hold approach, we find that our statistical trading systems possess superior risk-return characteristics. Finally, a deep dive analysis shows synchronous developments between the chosen number of principal components and the S&P 500 index.

Bayesian Analysis of Stochastic Betas

2005 ◽  
Vol 40 (4) ◽  
pp. 747-778 ◽  
Author(s):  
Gergana Jostova ◽  
Alexander Philipov
Keyword(s):  
Asset Pricing ◽  
Stock Returns ◽  
Idiosyncratic Volatility ◽  
Firm Level ◽  
Conditional Capm ◽  
Asset Pricing Anomalies ◽  
Proposed Model ◽  
Out Of Sample ◽  
Market Beta ◽  
Rolling Regression

AbstractWe propose a mean-reverting stochastic process for the market beta. In a simulation study, the proposed model generates significantly more precise beta estimates than GARCH betas, betas conditioned on aggregate or firm-level variables, and rolling regression betas, even when the true betas are generated based on these competing specifications. Our model significantly improves out-of-sample hedging effectiveness. In asset pricing tests, our model provides substantially stronger support for the conditional CAPM relative to competing beta models and helps resolve asset pricing anomalies such as the size, book-to-market, and idiosyncratic volatility effects in the cross section of stock returns.

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