Journal of Finance and Data Science最新文献

In an environment characterized by stochastic variances and correlations, we demonstrate through construction of the equilibrium index option value from constituent components, that the generalized PDE identifies the stochastic elements differentially affecting index option prices relative to prices of aggregated constituent stock options. A unified treatment of the generalized partial differential system for index and constituent stock options in Theorem 1 illustrates that nonlinear interactive terms emanating from stochastic correlation affect index option price and return essentially different from contributions to the aggregated risks of the constituent stock options. Our study contributes to the growing evidence of priced correlation risk in markets for index and constituent stock options.

Theorem 1 illustrates the pricing differential, while Proposition 1 illustrates that the pricing differential produces a quantifiable metric of the measure of the nonlinear interactive terms. The quantifiable metric is constructed from the difference between the model free implied variance of the index and a weighted aggregate of the model free implied variances of the constituent stocks. Proposition 2 identifies that index variance risk premium includes additional significant contributions from the nonlinear interactive risks not present in the aggregated returns of the constituent stocks. The nonlinear interactive risks produce a wedge between the instantaneous expected excess index and aggregated stock option returns.

This paper aims to analyze the impact of public education expenditures on GDP per capita of Tunisia and Morocco during the period 1980–2015. This study is based on the Auto-Regressive Distributive Lags (ARDL) approach that is proposed by Pesaran et al. The empirical estimate yields interesting results. In the short term, the relationship between public spending on education and GDP per capita in Morocco is positive while it is negative in Tunisia. In the long term, by contrast, public expenditure on education serves to increase the GDP per capita of the two countries, but more intensively so in Morocco than in Tunisia.

I have extended the arithmetic and logarithmic equations of the daily return by including daily dividend. To do this, firstly, I have mathematically broadened the scope of the two mostly used formulas of daily return by including daily dividend. Next, I have developed a couple of daily dividend estimation models from both pre and post stockholders' perspective. While developing those models, I have functionally used the compounding factors of time value theory. Finally, I have empirically examined the statistical robustness of Model-1. The findings of the study revealed that inclusion of daily dividend significantly increased the daily and monthly arithmetic and logarithmic returns of the securities. However, after inclusion of daily dividend, the long run variances of the both arithmetic return series remains same whereas the long run variances of both logarithmic return series significantly turns down to around zero percent direct a sharp decline of the risk of logarithmic return. Moreover, after inclusion of daily dividend the Value at Risk (VaR) of the daily logarithmic return declines sharply validates Model 1 for computing the daily logarithmic return.

Return smoothing and performance persistence are both sources of autocorrelation in hedge fund returns. The practice of pre-processing the data in order to remove smoothing before conducting performance analysis also affects the predictability of hedge fund returns. This paper develops a Bayesian framework for the performance evaluation of hedge funds that simultaneously accounts for smoothing, time-varying performance and factor loadings, and the short-lived nature of reported returns. Simulation evidence reveals that “unsmoothing” predictable, persistent hedge fund returns reduces the ability to detect performance persistence in the second step of the analysis. Empirically, smoothing generates severe biases in standard estimates of abnormal performance, factor loadings, and idiosyncratic volatility. In particular, for funds with high systematic risk, a standard deviation increase in smoothing implies an upward bias in α in excess of 2% annually and a downward bias in equity market beta of more than 20%. For funds with low systematic risk exposure, the smoothing bias is most apparent in estimates of idiosyncratic volatility.

Investors in financial markets are often at a loss when facing a huge range of products. For financial institutions also, how to recommend products to the right investors, especially those without previous investment records is problematic. In this paper, we develop and apply a personalized recommendation system for the equity funds market, based on the idea of transfer learning. First, using modern portfolio theory, a profile of equity funds and investors is created. Then, the profile of investors in the stock market is applied to the fund market by the idea of transfer learning. Finally, a utility-based recommendation algorithm based on prospect theory is proposed and the performance of the method is verified by testing it on actual transaction data. This study provides a reference for financial institutions to recommend products and services to the long tail customers.

Time Dependent Kernel Density Estimation (TDKDE) used in modelling time-varying phenomenon requires two input parameters known as bandwidth and discount to perform. A Maximum Likelihood Estimation (MLE) procedure is commonly used to estimate these parameters in a set of data but this method has a weakness; it may not produce stable kernel estimates. In this article, a novel estimation procedure is developed using Artificial Neural Networks which eliminates this inherent issue. Moreover, evaluating the performance of the kernel estimation in terms of the uniformity of Probability Integral Transform (PIT) shows a significant improvement using the proposed method. A real-life application of TDKDE parameter estimation on NASDQ stock returns validates the flawless performance of the new technique.

The purpose of predictive stock price systems is to provide abnormal returns for financial market operators and serve as a basis for risk management tools. Although the Efficient Market Hypothesis (EMH) states that it is not possible to anticipate market movements consistently, the use of computationally intensive systems that employ machine learning algorithms is increasingly common in the development of stock trading mechanisms. Several studies, using daily stock prices, have presented predictive system applications trained on fixed periods without considering new model updates. In this context, this study uses a machine learning technique called Support Vector Regression (SVR) to predict stock prices for large and small capitalisations and in three different markets, employing prices with both daily and up-to-the-minute frequencies. Prediction errors are measured, and the model is compared to the random walk model proposed by the EMH. The results suggest that the SVR has predictive power, especially when using a strategy of updating the model periodically. There are also indicative results of increased predictions precision during lower volatility periods.

This paper promulgates an innovative measure of market immediacy; that is, Coefficient of Elasticity Trading Three (CET3). The data from Nairobi Securities Exchange has been used to estimate market immediacy (proxied by three versions of CET; that is, CET1, CET2 and CET3). On the other hand, macroeconomic data on economic growth, general government final consumption expenditure, foreign direct investment (FDI) and inflation for the same period were obtained from Kenya National Bureau of Statistics. An Ordinary Least Square (OLS) regression with economic growth as a regressand and market immediacy and macroeconomic array of conditional information set as regressors have been used to determine which version of CET is more robust than the rest. The diagnostic tests consisted among others Granger causality, Augmented Dicker Fuller test (ADF) and Autoregressive Distributed Lag (ARDL) model analysis. The OLS regression p-values, Adjusted R² and standard errors demonstrate that CET3 is a better measure of market immediacy than CET1 and CET2.

The arrival of Big Data strategies is threatening the latest trends in financial regulation related to the simplification of models and the enhancement of the comparability of approaches chosen by financial institutions. Indeed, the intrinsic dynamic philosophy of Big Data strategies is almost incompatible with the current legal and regulatory framework as illustrated in this paper. Besides, as presented in our application to credit scoring, the model selection may also evolve dynamically forcing both practitioners and regulators to develop libraries of models, strategies allowing to switch from one to the other as well as supervising approaches allowing financial institutions to innovate in a risk mitigated environment. The purpose of this paper is therefore to analyse the issues related to the Big Data environment and in particular to machine learning models highlighting the issues present in the current framework confronting the data flows, the model selection process and the necessity to generate appropriate outcomes.

Travel and leisure recorded a consecutive robust growth and become among the fastest economic sectors in the world. Various forecasting models are proposed by researchers that serve as an early recommendation for investors and policy makers. Numerous studies proposed distinct forecasting models to predict the dynamics of this sector and provide early recommendation for investors and policy makers. In this paper, we compare the performance of smooth transition autoregressive (STAR) and linear autoregressive (AR) models using monthly returns of Turkey and FTSE travel and leisure index from April 1997 to August 2016. MSCI world index used as a proxy of the overall market. The result shows that nonlinear LSTAR model cannot improve the out-of-sample forecast of linear AR model. This finding demonstrates little to be gained from using LSTAR model in the prediction of travel and leisure stock index.