Journal of Financial Management, Markets and Institutions

The success of investors in obtaining huge financial rewards from the stock market depends on their ability to predict the direction of the stock market index. The purpose of this study is to evaluate the efficacy of several ensemble prediction models (Boosted, RUS-Boosted, Subspace Disc, Bagged, and Subspace KNN) in predicting the daily direction of the Johannesburg Stock Exchange (JSE) All-Share index compared to other commonly used machine learning techniques including support vector machines (SVM), logistic regression and $k$-nearest neighbor (KNN). The findings in this study show that, among all ensemble models, Boosted algorithm is the best performer followed by RUS-Boosted. When compared to the other techniques, ensemble technique (represented by Boosted) outperformed these techniques, followed by KNN, logistic regression and SVM, respectively. These findings suggest that investors should include ensemble models among the index prediction models if they want to make huge profits in the stock markets. However, not all investors can benefit from this as models may suffer from alpha decay as more and more investors use them, implying that the successful algorithms have limited shelf life.

Investment certificates are securitized derivatives built as a combination of financial instruments. The financial engineering process aims to create new payoff profiles that allow investors to diversify or to hedge the risk of their portfolios. Such instruments are relatively challenging to price, as highlighted in the recent publications for other European markets. The aim of this paper is to analyze whether in the Italian market there are also differences between the quoted price and the estimated price obtained applying a standard pricing approach well known in the literature. In particular, we analyze three representative certificates, belonging to the classes of target coupon certificates and autocallable certificates, that have been most appreciated by the investors during the last years. To evaluate the price, we propose a Monte Carlo approach that computes directly the payoff of the certificates on a set of scenarios for the evolution of the underlying asset. Moreover, studying the payoff profile of the certificates, we investigate and comment on the recent regulatory debate on “complexity”. We show that complexity, the new parameter behind return and risk, should not be necessarily associated with the engineering level of the financial products and that, sometimes, complexity is not associated with risk.

Accurate Value at Risk (VaR) estimations are crucial for the robustness and stability of a financial system. Even though significant advances have been made in the field of risk modeling, many crises have emerged during the same period, and an explanation for this is that the advanced models are not widely applied in the financial industry due to their mathematical complexity. In contrast to the mathematically complex models that torture the data in the output stage, we suggest a new approach that filters the data inputs, based on Technical Analysis (TA) signals. When the trading signals suggest that the conditions are positive (negative) for investments we use data from the previously documented positive (negative) periods in order to calculate the VaR. In this way, we use input data that are more representative of the financial conditions under examination and thus VaR estimations are more accurate and more representative (nonprocyclical) than the conventional models’ estimation that use the last nonfiltered $x$-day observations. Testing our assumptions in the US stock market for the period 2000–2017, the empirical data confirmed our hypothesis. Moreover, we suggest specific legislative adjustments that contribute to more accurate and representative VaR estimations: (i) an extra backtesting procedure at a lower than the 99% confidence level as a procyclicality test and (ii) to ease the minimum requirement of 250 observations that is currently the input threshold because it leads to less accurate VaR estimations.

We examine the information content of strategic-plans’ long-term growth targets (SPLTG) and of strategic-plans’ forecast horizons (SPFH). Using a sample of 224 strategic plan presentations by Italian listed companies during the period 2002–2018, we provide evidence that the SPLTG conveys credible and useful information to investors. We also assume that longer forecast horizons are more uncertain and we find that stock price reaction is negatively associated with long-term forecast horizons. Then, we investigate whether SPLTG presented in conjunction with long-term SPFH are perceived as less credible. The findings document that investors perceive long-term growth targets as credible regardless of the SPFH length. Our study contributes to the current debate on the use of strategic plans as comprehensive disclosure able to provide credible and useful information.

This paper analyzes the extent to which selection explains the observed discrepancy between solicited and unsolicited ratings. I propose a model of selection with truth telling rating agencies and borrowers with the ability to veto the revelation of the rating. The observed difference between the two categories of ratings in different sectors is in line with the prediction of the model. In the sovereign market there is a positive selection of borrowers into unsolicited ratings whereas other sectors have, on the contrary, lower unsolicited rating grades than those solicited.