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on Financial Markets |
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Issue of 2024‒08‒12
nine papers chosen by |
| By: | Helmut Wasserbacher; Martin Spindler |
| Abstract: | Why do companies choose particular capital structures? A compelling answer to this question remains elusive despite extensive research. In this article, we use double machine learning to examine the heterogeneous causal effect of credit ratings on leverage. Taking advantage of the flexibility of random forests within the double machine learning framework, we model the relationship between variables associated with leverage and credit ratings without imposing strong assumptions about their functional form. This approach also allows for data-driven variable selection from a large set of individual company characteristics, supporting valid causal inference. We report three findings: First, credit ratings causally affect the leverage ratio. Having a rating, as opposed to having none, increases leverage by approximately 7 to 9 percentage points, or 30\% to 40\% relative to the sample mean leverage. However, this result comes with an important caveat, captured in our second finding: the effect is highly heterogeneous and varies depending on the specific rating. For AAA and AA ratings, the effect is negative, reducing leverage by about 5 percentage points. For A and BBB ratings, the effect is approximately zero. From BB ratings onwards, the effect becomes positive, exceeding 10 percentage points. Third, contrary to what the second finding might imply at first glance, the change from no effect to a positive effect does not occur abruptly at the boundary between investment and speculative grade ratings. Rather, it is gradual, taking place across the granular rating notches ("+/-") within the BBB and BB categories. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.18936 |
| By: | Elie Bouri (Adnan Kassar School of Business, Lebanese American University, Lebanon); Matteo Foglia (Department of Economics and Finance, University of Bari “Aldo Moro†, Italy); Sayar Karmakar (Department of Statistics, University of Florida, USA); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | Based on the rationale that returns and volatility are interrelated, we apply a multilayer network framework involving the return layer and volatility layer of cryptocurrencies, NFTs, and DeFi assets over the period January 1, 2018 - January 23, 2024. The results show significant connectedness in each of the return and volatility layers, with major cryptocurrencies such as Bitcoin and Ethereum playing a central role. Large spikes in the level of connectedness are noticed around COVID-19 pandemic and Russia-Ukraine conflict, and Bitcoin and Ethereum emerge are net transmitters of returns and volatility shocks, emphasizing their significant role around these crisis periods. Notably, a strong positive rank correlation exists between the return and volatility layers, highlighting the significant risk-return relationship in the digital asset class. The findings suggest that economic actors should not ignore the interconnectedness between the return and volatility layers in the system of cryptocurrencies, NFTs, and DeFi assets for the sake of a comprehensive analysis of information flow. Otherwise, a share of the information flow concerning the return-volatility nexus across these digital assets would be missed, possibly leading to inferences regarding asset pricing, portfolio allocation, and risk management. |
| Keywords: | Multilayer networks, Spillover effects, return-volatility, cryptocurrencies, NFTs, DeFi, COVID-19, Russia-Ukraine conflict |
| JEL: | C32 G10 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202432 |
| By: | Luigi Bocola; Alessandro Dovis; Kasper Jørgensen; Rishabh Kirpalani |
| Abstract: | This paper uses high frequency data to detect shifts in financial markets' perception of the Federal Reserve stance on inflation. We construct daily revisions to expectations of future nominal interest rates and inflation that are priced into nominal and inflation-protected bonds, and find that the relation between these two variables-positive and stable for over twenty years-has weakened substantially over the 2020-2022 period. In the context of canonical monetary reaction functions considered in the literature, these results are indicative of a monetary authority that places less weight on inflation stabilization. We augment a standard New Keynesian model with regime shifts in the monetary policy rule, calibrate it to match our findings, and use it as a laboratory to understand the drivers of U.S. inflation post 2020. We find that the shift in the monetary policy stance accounts for half of the observed increase in inflation. |
| JEL: | E58 G13 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32620 |
| By: | Gabriel Chodorow-Reich; Plamen T. Nenov; Vitor Santos; Alp Simsek |
| Abstract: | We use data on stock portfolios of Norwegian households to show that stock market wealth increases entrepreneurship by relaxing financial constraints. Our research design isolates idiosyncratic variation in household-level stock market returns. An increase in stock market wealth increases the propensity to start a firm, with the response concentrated in households with moderate levels of financial wealth, for whom a 20 percent increase in wealth due to a positive stock return increases the likelihood to start a firm by about 20%, and in years when the aggregate stock market return in Norway is high. We develop a method to study the effect of wealth on firm outcomes that corrects for the bias introduced by selection into entrepreneurship. Higher wealth causally increases firm profitability, an indication that it relaxes would-be entrepreneurs’ financial constraints. Consistent with this interpretation, the pass-through from stock wealth into equity in the new firm is one-for-one. |
| JEL: | E22 E44 G50 L26 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32643 |
| By: | Marina Albanese; Guglielmo Maria Caporale; Ida Colella; Nicola Spagnolo |
| Abstract: | This paper examines the impact of transition and physical climate risk on stock markets using, for the first time in this context, the annual CCPI index calculated by Germanwatch as well as its components (in addition to a wide range of other indices) for 48 countries from 2007 to 2023. Specifically, a balanced panel VAR model is estimated to obtain impulse responses for the whole set of countries considered as well as for a subset including the EU-28 only; other methods such as Forecast Error Variance Decomposition and Local Projections (Jorda, 2005, 2022) are then applied for robustness checks. The results suggest a positive impact of transition risk on stock returns and a negative one of physical risk, especially in the short term. Further, while physical risk appears to have an immediate impact, transition risk is shown to affect stock markets also over a longer time horizon. Finally, national climate policies seem to be more effective when implemented within a supranational framework as in the case of the EU-28. |
| Keywords: | climate change, physical risk, transition risk, stock markets, balanced panel VAR, impulse response analysis, local projections |
| JEL: | C33 G12 G18 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11184 |
| By: | Daiya Mita (Nomura Asset Management Co, ltd., Graduate School of Economics, The University of Tokyo); Akihiko Takahashi (Graduate School of Economics, The University of Tokyo) |
| Abstract: | This study proposes a novel equity investment strategy that effectively integrates artificial intelligence (AI) techniques, multi factor models and financial technical indicators. To be practically useful as an investment fund, the strategy is designed to achieve high investment performance without losing interpretability, which is not always the case especially for complex models based on artificial intelligence. Specifically, as an equity long (buying) strategy, this paper extends a five factor model in Fama & French [1], a well-known finance model for its explainability to predict future returns by using a gradient boosting machine (GBM) and a state space model. In addition, an index futures short (selling) strategy for downside hedging is developed with IF-THEN rules and three technical indicators. Combining individual equity long and index futures short models, the strategy invests in high expected return equities when the expected return of the portfolio is positive and also the market is expected to rise, otherwise it shorts (sells) index futures. To the best of our knowledge, the current study is the first attempt to develop an equity investment strategy based on a new predictable five factor model, which becomes successful with effective use of AI techniques and technical indicators. Finally, empirical analysis shows that the proposed strategy outperforms not only the baseline buy-and-hold strategy, but also typical mutual funds for the Japanese equities. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:cfi:fseres:cf588 |
| By: | Parley R Yang; Alexander Y Shestopaloff |
| Abstract: | We demonstrate the use of Conditional Variational Encoder (CVAE) to improve the forecasts of daily stock volume time series in both short and long term forecasting tasks, with the use of advanced information of input variables such as rebalancing dates. CVAE generates non-linear time series as out-of-sample forecasts, which have better accuracy and closer fit of correlation to the actual data, compared to traditional linear models. These generative forecasts can also be used for scenario generation, which aids interpretation. We further discuss correlations in non-stationary time series and other potential extensions from the CVAE forecasts. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.19414 |
| By: | Paolo Barucca; Flaviano Morone |
| Abstract: | We process private equity transactions to predict public market behavior with a logit model. Specifically, we estimate our model to predict quarterly returns for both the broad market and for individual sectors. Our hypothesis is that private equity investments (in aggregate) carry predictive signal about publicly traded securities. The key source of such predictive signal is the fact that, during their diligence process, private equity fund managers are privy to valuable company information that may not yet be reflected in the public markets at the time of their investment. Thus, we posit that we can discover investors' collective near-term insight via detailed analysis of the timing and nature of the deals they execute. We evaluate the accuracy of the estimated model by applying it to test data where we know the correct output value. Remarkably, our model performs consistently better than a null model simply based on return statistics, while showing a predictive accuracy of up to 70% in sectors such as Consumer Services, Communications, and Non Energy Minerals. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.01818 |
| By: | Anishka Chauhan; Pratham Mayur; Yeshwanth Sai Gokarakonda; Pooriya Jamie; Naman Mehrotra |
| Abstract: | This paper presents price prediction models using Machine Learning algorithms augmented with Superforecasters predictions, aimed at enhancing investment decisions. Five Machine Learning models are built, including Bidirectional LSTM, ARIMA, a combination of CNN and LSTM, GRU, and a model built using LSTM and GRU algorithms. The models are evaluated using the Mean Absolute Error to determine their predictive accuracy. Additionally, the paper suggests incorporating human intelligence by identifying Superforecasters and tracking their predictions to anticipate unpredictable shifts or changes in stock prices . The predictions made by these users can further enhance the accuracy of stock price predictions when combined with Machine Learning and Natural Language Processing techniques. Predicting the price of any commodity can be a significant task but predicting the price of a stock in the stock market deals with much more uncertainty. Recognising the limited knowledge and exposure to stocks among certain investors, this paper proposes price prediction models using Machine Learning algorithms. In this work, five Machine learning models are built using Bidirectional LSTM, ARIMA, a combination of CNN and LSTM, GRU and the last one is built using LSTM and GRU algorithms. Later these models are assessed using MAE scores to find which model is predicting with the highest accuracy. In addition to this, this paper also suggests the use of human intelligence to closely predict the shift in price patterns in the stock market The main goal is to identify Superforecasters and track their predictions to anticipate unpredictable shifts or changes in stock prices. By leveraging the combined power of Machine Learning and the Human Intelligence, predictive accuracy can be significantly increased. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.02236 |