Journal of Finance and Data Science最新文献

In this paper, we find that the conflict of interest between loan holders and bondholders is positively related to bond IPO underpricing, which serves as a compensation to the initial bond investors. We construct four proxies for the conflict between loan holders and bondholders, namely, a loan covenants index, the outstanding loan amount, the number of lead banks, and the loan remaining maturity. Our empirical tests show that all four variables are positively related to bond IPO underpricing, indicating that the loan structure of firms has a real impact on the pricing of their bond IPOs.

Can deep reinforcement learning algorithms be exploited as solvers for optimal trading strategies? The aim of this work is to test reinforcement learning algorithms on conceptually simple, but mathematically non-trivial, trading environments. The environments are chosen such that an optimal or close-to-optimal trading strategy is known. We study the deep deterministic policy gradient algorithm and show that such a reinforcement learning agent can successfully recover the essential features of the optimal trading strategies and achieve close-to-optimal rewards.

We construct the French nominal yield curve using Svensson³³ methodology and all available public data of French nominal government debt securities—Obligations Assimilables du Trésor (OATs). Our sample period starts in October 1987 and ends in April 2018. We find that the functioning of the French sovereign bond market has improved dramatically following the onset of the euro area and has been functioning reasonably well since then, with the exceptions of the Global Financial Crisis period and the European sovereign crisis period. We also find that, the French nominal on-the-run securities have, on average, a negligible liquidity premium, in sharp contrast to the U.S. nominal Treasury market, where such a premium is sizable. On average, the level and the slope of the French zero-coupon rates have been decreasing since the Global Financial Crisis.

This paper presents the first-ever comparison of the forecasting power of two types of narratives: articles in a major daily newspaper and regular research reports released by professional forecasters. The applied testing methodology developed in ²² and extended in this paper includes two natural language processing (NLP) techniques – the sentiment analysis and the wordscores model – that are used to convert the text corpora into the NLP indices. These indices are explanatory variables in linear regression, Granger causality test, vector autoregressive model and random forest model. The paper extends this methodology by applying Latent Dirichlet Allocation (LDA) to the newspaper corpus to filter out articles that discuss topics not relevant for economic and financial analysis. The forecasting test is conducted for two major banks in Poland – BZ WBK and mbank and for major daily newspaper Rzeczpospolita, in Polish. It is shown that mbank narratives have the best forecasting power, while BZ WBK and Rzeczpospolita trade second and third place depending on the model applied. In the vast majority of analyzed cases adding an NLP index to the model improves the forecast accuracy. The answer to the title question is – it depends. Before paying for economic research asset managers are advised to apply methods such as presented in this paper to evaluate whether sell-side research offers any forecasting value in comparison with a newspaper.

This paper investigates the asset pricing implications of rollover risk, i.e., the risk that firms might not be able to refinance their due liabilities. I find that firm-specific rollover risk coupled with deteriorating credit market conditions significantly increase firms' credit spreads. During the 2008–2009 financial crisis period, the one-year CDS spreads for high rollover risk firms are 32–72 basis points higher than the spreads of low rollover risk firms. Longer maturity CDS spreads show similar patterns with smaller magnitudes. During normal periods, however, CDS spreads are mostly explained by fundamental variables and rollover risk is not a significant determinant. Similar rollover risk effect is also observed in other financial markets, including corporate bond, stock, and options markets.

In this paper, the daily returns of the S&P 500 stock market index are predicted using a variety of different machine learning methods. We propose a new multinomial classification approach to forecasting stock returns. The multinomial approach can isolate the noisy fluctuation around zero return and allows us to focus on predicting the more informative large absolute returns. Our in-sample and out-of-sample forecasting results indicate significant return predictability from a statistical point of view. Moreover, all the machine learning methods considered outperform the benchmark buy-and-hold strategy in a real-life trading simulation. The gradient boosting machine is the top-performer in terms of both the statistical and economic evaluation criteria.

We propose a machine learning toolkit applied to the detection of rare events, namely banking crises. For this purpose, we consider a broad set of macroeconomic series (credit-to-GDP gap, house prices, stock prices, inflation rates, long-term and short-term interest rates, etc.), in combination with their leads and lags, various filtering methodologies, and datascience models that complement time series analysis. The main advantages of the approach are its robustness, its flexibility and its prediction performance. Based on the best model specification, our methodology allows to compute an indicator for the probability of banking crisis along with an alert threshold up to 6 quarters ahead in real time for various developed economies.

There has been much debate about whether returns on financial assets, such as stock returns or commodity returns, are predictable; however, few studies have investigated cryptocurrency return predictability. In this article we examine whether bitcoin returns are predictable by a large set of bitcoin price-based technical indicators. Specifically, we construct a classification tree-based model for return prediction using 124 technical indicators. We provide evidence that the proposed model has strong out-of-sample predictive power for narrow ranges of daily returns on bitcoin. This finding indicates that using big data and technical analysis can help predict bitcoin returns that are hardly driven by fundamentals.

To better understand the relation between public markets and private equity, we consider quadratic hedging strategies to replicate the typical payment stream pattern associated with private equity funds by traded factors. Our methodology is inspired by the risk-minimization framework developed in financial mathematics and applies the componentwise L₂ Boosting machine learning technique to empirically identify feasible replication strategies. The application to US venture capital fund data further draws on a stability selection procedure to enhance model sparsity. Interestingly a natural connection to the famous Kaplan and Schoar (2005) public market equivalent approach can be established.

The Publisher regrets that this article is an accidental duplication of an article that has already been published in <JFDS, 5/2(2019) 116-125>,http://dx.doi.org/10.1016/j.jfds.2019.04.001.The duplicate article has therefore been withdrawn.

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