scholarly journals Can Multistep Nonparametric Regressions Beat Historical Average in Predicting Excess Stock Returns?

2021 ◽  
Vol 12 (5) ◽  
pp. 71
Author(s):  
Najrin Khanom
Keyword(s):  
Stock Returns ◽  
Predictive Power ◽  
Predictive Ability ◽  
Predictive Performance ◽  
Ordinary Least Squares ◽  
Nonparametric Model ◽  
Predictive Variables ◽  
Out Of Sample ◽  
Excess Stock Returns ◽  
Non Parametric

Several economic and financial variables are said to have predictive power over excess stock returns. Empirically there is little consensus among academics, whether these variables have predictive power or not. Results are often sensitive to the econometric model of choice. The econometric models can produce biased results due to the high degree of persistence in predictive variables. Apart from high persistence, the relationship between stock return and the predictive variable may also be misspecified in the model. In order to address possible non-linearities and endogeneity between the residuals and persistent independent variables in predictive regressions, multi-step non-parametric and semiparametric regressions are explored in this paper. In these regressions, the conditional mean and the residuals are estimated separately and then added to obtain the predicted excess stock returns. Goyal and Welch's (2008) predictive variables are used to predict excess S&P 500 returns. The predictive performance of both in-sample and out-of-sample of the two proposed models are compared with the historical average, Ordinary Least Squares (OLS) and non-parametric regressions. The performance of the models is evaluated using Root Mean Squared Errors (RMSEs). The explored models, particularly the two-step nonparametric model, outperform the compared models in-sample. Out-of-sample several variables are found to have predictive ability.

scholarly journals A new EDAS-based in-sample-out-of-sample classifier for risk-class prediction

2019 ◽  
Vol 57 (2) ◽  
pp. 314-323 ◽  
Author(s):  
Jamal Ouenniche ◽  
Oscar Javier Uvalle Perez ◽  
Aziz Ettouhami
Keyword(s):  
Predictive Analytics ◽  
Predictive Performance ◽  
Data Set ◽  
Content Type ◽  
Risk Class ◽  
Design Elements ◽  
Classification Framework ◽  
Out Of Sample ◽  
Parametric Classifier ◽  
Non Parametric

PurposeNowadays, the field of data analytics is witnessing an unprecedented interest from a variety of stakeholders. The purpose of this paper is to contribute to the subfield of predictive analytics by proposing a new non-parametric classifier.Design/methodology/approachThe proposed new non-parametric classifier performs both in-sample and out-of-sample predictions, where in-sample predictions are devised with a new Evaluation Based on Distance from Average Solution (EDAS)-based classifier, and out-of-sample predictions are devised with a CBR-based classifier trained on the class predictions provided by the proposed EDAS-based classifier.FindingsThe performance of the proposed new non-parametric classification framework is tested on a data set of UK firms in predicting bankruptcy. Numerical results demonstrate an outstanding predictive performance, which is robust to the implementation decisions’ choices.Practical implicationsThe exceptional predictive performance of the proposed new non-parametric classifier makes it a real contender in actual applications in areas such as finance and investment, internet security, fraud and medical diagnosis, where the accuracy of the risk-class predictions has serious consequences for the relevant stakeholders.Originality/valueOver and above the design elements of the new integrated in-sample-out-of-sample classification framework and its non-parametric nature, it delivers an outstanding predictive performance for a bankruptcy prediction application.

scholarly journals Firm returns and network centrality

2019 ◽  
Vol 9 (3) ◽  
pp. 74-82
Author(s):  
Zornitsa Todorova
Keyword(s):  
Stock Returns ◽  
Risk Premium ◽  
Predictive Power ◽  
Empirical Relationship ◽  
Investment Strategy ◽  
Network Centrality ◽  
Out Of Sample ◽  
Expected Stock Returns ◽  
Market Beta ◽  
Recent Financial Crisis

Using methods from graph theory and network analysis, this paper identifies, visualizes and analyzes a correlation network of residual stock returns for more than 5,000 US-based publicly traded firms. Building on prior work by Billio et al. (2012), the paper computes a systemic measure of network centrality using principal components analysis. Two main questions are addressed: 1) What is the empirical relationship between expected stock returns and network centrality? and 2) Does network centrality have predictive power to identify firms, which are most at risk during systemic events? First, the paper finds that network centrality has substantial predictive power in out-of-sample tests related to the recent financial crisis. Second, firms that are more central in the network earn higher returns than firms that are located in the periphery. The paper rationalizes this finding by arguing that central firms are characterized by higher market risk because they are more exposed to idiosyncratic shocks passing through the network. Finally, the paper develops a novel factor-mimicking portfolio, weighted by centrality scores. The investment strategy earns an annualized risk premium of 3.38 % controlling for market beta, size and book-to-market.

scholarly journals International evidence on stock returns and dividend growth predictability using dividend yields

2020 ◽  
Vol 31 (84) ◽  
pp. 473-489
Author(s):  
Ana Monteiro ◽  
Helder Sebastião ◽  
Nuno Silva
Keyword(s):  
Stock Returns ◽  
Capital Markets ◽  
Predictive Ability ◽  
Sample Analysis ◽  
The Us ◽  
Out Of Sample ◽  
Dividend Smoothing ◽  
The Uk ◽  
The Relationship ◽  
Dividend Growth

ABSTRACT This paper examines stock returns and dividend growth predictability using dividend yields in seven developed markets: United States of America (US), United Kingdom (UK), Japan, France, Germany, Italy, and Spain. Altogether, these countries account for around 85% of the Morgan Stanley Capital International (MSCI) World Index. The use of the long time series with up-to-date data allows the comparison not only between countries, but also across periods, putting into perspective the existence or not of noticeable changes since the 1980’s. The majority of the literature on this topic is US-centered. This emphasis on the US is even more pronounced when it comes to examining the relationship between the dividend unpredictability and dividend smoothing. There is also the need to know if the relationships already documented for the post-Second World War (WWII) period still hold during the last three decades, when stock markets were subjected to a high level of turbulence worldwide. The relationship between dividend yields and returns and dividend growth is central to understand the functioning of capital markets, and has considerable implications for capital asset pricing and investment strategies. Overall, the results show that even for developed capital markets there is no clear pattern on the predictive ability of dividend yields on stock returns and dividend growth, instead these relationships seem to be time-dependent and country-specific. For each country, the predictive ability of the dividend yield is examined in a first-order structural VAR framework by applying bootstrap significance tests and the degree of dividend smoothing is assessed using four partial-adjustment models for the dividend behavior. Additionally, an out-of-sample analysis is conducted using pseudo-R2 and a normal mean squared prediction error (MSPE) adjusted statistic. For the post-WWII period, returns are predictable, but dividends are unpredictable in the US and the UK, while the opposite pattern is observed in Spain and Italy. In Germany, there is some evidence of short-term predictability for both returns and dividends, while in France only returns are predictable. In Japan, neither variable can be forecasted. The dividend smoothing results show that dividends are more persistent in the US and the UK, however, there is no clear connection between dividend smoothness and predictability for the other countries. An important conclusion to retain from the out-of-sample analysis is that the predictability of returns after the WWII, especially present in the US, appeared to have been missing in the last three decades, most probably due to the turmoil experienced by the stock markets during this last period.

scholarly journals Forecasting Capacity of ARIMA Models; A Study on Croatian Industrial Production and its Sub-sectors

2017 ◽  
Vol 20 (1) ◽  
pp. 81-99 ◽  
Author(s):  
Daniel Tomić ◽  
Saša Stjepanović
Keyword(s):  
Industrial Production ◽  
Moving Average ◽  
Predictive Ability ◽  
Predictive Performance ◽  
Arima Models ◽  
Important Indicator ◽  
Out Of Sample ◽  
Short And Long Term ◽  
Data Evolution

Abstract As one of the most important indicator for monitoring the production in industry as well as for directing investment decisions, industrial production plays important role within growth perspectives. Not only does the composition and/or fluctuation of the goods produced indicate the course of economic activity but it also reflects the changes in cyclical development of the economy thereby providing opportunity to macro-manage with early signs of (short-term) turning-points and (long-term) trend variations. In this paper, we compare univariate autoregressive integrated moving average (ARIMA) models of the Croatian industrial production and its subsectors in order to evaluate their forecasting features within short and long-term data evolution. The aim of this study is not to forecast industrial production but to analyze the out-of-sample predictive performance of ARIMA models on aggregated and disaggregated level inside different forecasting horizons. Our results suggest that ARIMA models do perform very well over the whole rage of the prediction horizons. It is mainly because univariate models often improve the predictive ability of their single component over the short horizons. In that manner ARIMA modelling could be used at least as a benchmark for more complex forecasting methods in predicting the movements of industrial production in Croatia.

scholarly journals Debt-by-Price Ratio, End-of-Year Economic Growth, and Long-Term Prediction of Stock Returns

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1550
Author(s):  
Parastoo Mousavi
Keyword(s):  
Economic Growth ◽  
Stock Returns ◽  
Government Debt ◽  
Nonparametric Model ◽  
Price Ratio ◽  
Role Of Government ◽  
Predictive Variables ◽  
Wide Range ◽  
Significant Difference

With the prominent role of government debt in economic growth in recent decades, one would expect that government debt alongside economic growth to be a risk factor priced in the time series of stock returns. In this paper, this idea is investigated by applying a nonparametric model, namely, a local-linear kernel smoother with the aim of forecasting long-term stock returns where the model and smoothing parameters are chosen by cross-validation. While a wide range of predictive variables are examined, we find that our newly introduced debt-by-price ratio and the third to fourth quarter economic growth are robust predictors of stock returns, beating the well-known predictive variables in the literature by a significant difference. The combination of these two covariates can explain almost 30% variation of stock returns at a one-year horizon. This is very crucial considering the difficulty in capturing even a small proportion of movements in stock returns.

How Does Google Search Affect the Stock Market? Evidence from Indian Companies

2021 ◽  
pp. 097226292098536
Author(s):  
Tariq Aziz ◽  
Valeed Ahmad Ansari
Keyword(s):  
Stock Returns ◽  
Stock Prices ◽  
Predictive Ability ◽  
Positive Influence ◽  
Market Sentiment ◽  
Price Pressure ◽  
Search Data ◽  
Online Searches ◽  
Excess Stock Returns ◽  
Google Search

Google search data has received considerable attention for its predictive ability in various social and economic outcomes. In the arena of investments, a surge in online searches indicates an enhanced interest of investors, particularly retail, in that company. In this article, we have examined the association between Google search and stock prices in a sample of Indian companies. The results suggest that an increase in Google search is positively related to future excess stock returns, liquidity and volatility. The positive influence of Google search on stock prices, however, is temporary and reverses in the next week. We further show that the market sentiment moderates the interconnection between Google searches and future excess stock returns. The findings are in consonance with the ‘price pressure hypothesis’ of Barber and Odean (2008, Review of Financial Studies, 21(2), 785–818).

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