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on Financial Markets |
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Issue of 2020‒03‒02
seven papers chosen by |
| By: | Leybourne, Stephen J; Harvey, David I; Taylor, AM Robert |
| Abstract: | Predictive regression methods are widely used to examine the predictability of (excess) stock returns by lagged financial variables characterised by unknown degrees of persistence and endogeneity. We develop new and easy to implement tests for predictability in these circumstances using regression t-ratios. The simplest possible test, optimal (under Gaussianity) for a weakly persistent and exogenous predictor, is based on the standard t-ratio from the OLS regression of returns on a constant and the lagged predictor. Where the predictor is endogenous, we show that the optimal, but infeasible, test for predictability is based on the t-ratio on the lagged predictor when augmenting the basic predictive regression above with the current period innovation driving the predictor. We propose a feasible version of this test, designed for the case where the predictor is an endogenous near-unit root process, using a GLS-based estimate of this innovation. We also discuss a variant of the standard t-ratio obtained from the predictive regression of OLS demeaned returns on the GLS demeaned lagged predictor. In the near-unit root case, the limiting null distributions of these three statistics depend on both the endogeneity correlation parameter and the local-to-unity parameter characterising the predictor. A feasible method for obtaining asymptotic critical values is discussed and response surfaces are provided. To develop procedures which display good size and power properties regardless of the degree of persistence of the predictor, we propose tests based on weighted combinations of the three t-ratios discussed above, where the weights are obtained using the p-values from a unit root test on the predictor. Using Monte Carlo methods we compare our preferred weighted test with the leading tests in the literature. These results suggest that, despite their simplicity, our weighted tests display very good finite sample size control and power across a range of persistence and endogeneity levels for the predictor, comparing very favourably with these extant tests. An empirical illustration using US stock returns is provided. |
| Keywords: | predictive regression, persistence, endogeneity, weighted statistics |
| Date: | 2020–02–24 |
| URL: | http://d.repec.org/n?u=RePEc:esy:uefcwp:26886&r=all |
| By: | Lior Sidi |
| Abstract: | Stock market prediction with forecasting algorithms is a popular topic these days where most of the forecasting algorithms train only on data collected on a particular stock. In this paper, we enriched the stock data with related stocks just as a professional trader would have done to improve the stock prediction models. We tested five different similarities functions and found co-integration similarity to have the best improvement on the prediction model. We evaluate the models on seven S&P stocks from various industries over five years period. The prediction model we trained on similar stocks had significantly better results with 0.55 mean accuracy, and 19.782 profit compare to the state of the art model with an accuracy of 0.52 and profit of 6.6. |
| Date: | 2020–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2002.05784&r=all |
| By: | Yan, Yu; Wang, Yiming |
| Abstract: | As one of the core models of finance, the consumer capital asset pricing model (CCAPM) has produced the puzzle of equity premium. In order to explain this problem and get a more realistic pricing formula, this paper uses constant absolute risk aversion coefficient (Cara) utility function and introduces heterogeneous consumers to improve the original model, and finally gets a more effective form and there is no original puzzle in this form. At the end of the article, the American data are used to verify the results. The regression results support this model very well. |
| Keywords: | CAPM;CARA;puzzle of equity premium |
| JEL: | G00 |
| Date: | 2020–02–05 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:98506&r=all |
| By: | Yunan Ye; Hengzhi Pei; Boxin Wang; Pin-Yu Chen; Yada Zhu; Jun Xiao; Bo Li |
| Abstract: | Portfolio management (PM) is a fundamental financial planning task that aims to achieve investment goals such as maximal profits or minimal risks. Its decision process involves continuous derivation of valuable information from various data sources and sequential decision optimization, which is a prospective research direction for reinforcement learning (RL). In this paper, we propose SARL, a novel State-Augmented RL framework for PM. Our framework aims to address two unique challenges in financial PM: (1) data heterogeneity -- the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary. To incorporate heterogeneous data and enhance robustness against environment uncertainty, our SARL augments the asset information with their price movement prediction as additional states, where the prediction can be solely based on financial data (e.g., asset prices) or derived from alternative sources such as news. Experiments on two real-world datasets, (i) Bitcoin market and (ii) HighTech stock market with 7-year Reuters news articles, validate the effectiveness of SARL over existing PM approaches, both in terms of accumulated profits and risk-adjusted profits. Moreover, extensive simulations are conducted to demonstrate the importance of our proposed state augmentation, providing new insights and boosting performance significantly over standard RL-based PM method and other baselines. |
| Date: | 2020–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2002.05780&r=all |
| By: | Ahmet Murat Ozbayoglu; Mehmet Ugur Gudelek; Omer Berat Sezer |
| Abstract: | Computational intelligence in finance has been a very popular topic for both academia and financial industry in the last few decades. Numerous studies have been published resulting in various models. Meanwhile, within the Machine Learning (ML) field, Deep Learning (DL) started getting a lot of attention recently, mostly due to its outperformance over the classical models. Lots of different implementations of DL exist today, and the broad interest is continuing. Finance is one particular area where DL models started getting traction, however, the playfield is wide open, a lot of research opportunities still exist. In this paper, we tried to provide a state-of-the-art snapshot of the developed DL models for financial applications, as of today. We not only categorized the works according to their intended subfield in finance but also analyzed them based on their DL models. In addition, we also aimed at identifying possible future implementations and highlighted the pathway for the ongoing research within the field. |
| Date: | 2020–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2002.05786&r=all |
| By: | Gregory Brown; Robert S. Harris; Wendy Hu; Tim Jenkinson; Steven N. Kaplan; David T. Robinson |
| Abstract: | Private equity performance, both for buyouts and venture capital, has been highly cyclical: periods of high fundraising have been followed by periods of low performance. Despite this seemingly predictable variation, we find modest gains, at best, to pursuing realistic, investable strategies that time capital commitments to private equity. This occurs, in part, because investors can only time their commitments to funds; they cannot time when commitments are called or when investments are exited. There is a high degree of time-series correlation in net cash flows even across commitment strategies that allocate capital in a very different manner over time. |
| JEL: | G23 G24 |
| Date: | 2020–02 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:26755&r=all |
| By: | L\'ester Alfonso; Danahe E. Garcia-Ramirez; Ricardo Mansilla; C\'esar A. Terrero-Escalante |
| Abstract: | In this paper, a statistical analysis of high frequency fluctuations of the IPC, the Mexican Stock Market Index, is presented. A sample of tick-to-tick data covering the period from January 1999 to December 2002 was analyzed, as well as several other sets obtained using temporal aggregation. Our results indicates that the highest frequency is not useful to understand the Mexican market because almost two thirds of the information corresponds to inactivity. For the frequency where fluctuations start to be relevant, the IPC data does not follows any alpha-stable distribution, including the Gaussian, perhaps because of the presence of autocorrelations. For a long range of lower-frequencies, but still in the intra-day regime, fluctuations can be described as a truncated L\'evy flight, while for frequencies above two-days, a Gaussian distribution yields the best fit. Thought these results are consistent with other previously reported for several markets, there are significant differences in the details of the corresponding descriptions. |
| Date: | 2020–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2002.05697&r=all |