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on Financial Markets |
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Issue of 2023‒12‒04
seven papers chosen by |
| By: | Scheicher, Martin |
| Abstract: | The trading of bonds and swaps largely relies on bank dealers as core market-makers. Dealers provide liquidity and trade the instruments with smaller or less active firms, in part by using their own balance sheets for inventory holding or hedging purposes. The reforms carried out in the aftermath of the global financial crisis (GFC) and the low interest rate environment have extensively changed the mechanisms and costs of trading fixed income instruments. This paper sets out to analyse the structure of trading in key over-the-counter (OTC) fixed income markets. We focus on three questions: (1) how are bonds and swaps currently traded and how liquid are these markets?, (2) how do the structural changes affect the dealer business model and market functioning?, and (3) how did the coronavirus (COVID-19) shock in March 2020 affect the OTC bond and swap market in its new post-reform set-up? To answer these questions, we combine an institutional and research perspective with a focus on key EU markets. We use public data and findings from the rich body of academic literature to describe the dealer business model and its post-GFC evolution. Overall, we argue that OTC fixed income trading is becoming “faster” due to the progress of electronic trading and the rise of non-bank traders, which has led bank dealers to make some adjustments to their market-making activities. The ongoing challenges faced in ensuring resilient provision of liquidity were also highlighted by the US bond market dislocation in March 2020. JEL Classification: G12, G15 |
| Keywords: | bonds, dealers, fixed income, liquidity, market structure, swaps |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:srk:srkops:202324&r=fmk |
| By: | Andre, Peter; Schirmer, Philipp; Wohlfart, Johannes |
| Abstract: | Investors' return expectations are pivotal in stock markets, but the reasoning behind these expectations remains a black box for economists. This paper sheds light on economic agents' mental models - their subjective understanding - of the stock market, drawing on surveys with the US general population, US retail investors, US financial professionals, and academic experts. Respondents make return forecasts in scenarios describing stale news about the future earnings streams of companies, and we collect rich data on respondents' reasoning. We document three main results. First, inference from stale news is rare among academic experts but common among households and financial professionals, who believe that stale good news lead to persistently higher expected returns in the future. Second, while experts refer to the notion of market efficiency to explain their forecasts, households and financial professionals reveal a neglect of equilibrium forces. They naively equate higher future earnings with higher future returns, neglecting the offsetting effect of endogenous price adjustments. Third, a series of experimental interventions demonstrate that these naive forecasts do not result from inattention to trading or price responses but reflect a gap in respondents' mental models - a fundamental unfamiliarity with the concept of equilibrium. |
| Keywords: | Mental models, return expectations |
| JEL: | D83 D84 G11 G12 G41 G51 G53 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:safewp:279782&r=fmk |
| By: | Zhenyu Gao; Wenxi Jiang; Wei A. Xiong; Wei Xiong |
| Abstract: | Despite the dominance of retail investors in the Chinese stock market, there’s a conspicuous absence of price momentum in weekly and monthly returns. This study uncovers the presence of price momentum in daily returns and, through a systematic analysis of trading heterogeneity among investors, links daily momentum to the attention and trading activities of new investors—a phenomenon particularly significant in emerging stock markets. Furthermore, our findings indicate the existence of daily price momentum in various other emerging markets, contrasting with its relative scarcity in developed ones. |
| JEL: | G02 G4 G40 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31839&r=fmk |
| By: | Ryan Chipwanya |
| Abstract: | The stock market has been established since the 13th century, but in the current epoch of time, it is substantially more practicable to anticipate the stock market than it was at any other point in time due to the tools and data that are available for both traditional and algorithmic trading. There are many different machine learning models that can do time-series forecasting in the context of machine learning. These models can be used to anticipate the future prices of assets and/or the directional bias of assets. In this study, we examine and contrast the effectiveness of three different machine learning algorithms, namely, logistic regression, decision tree, and random forest to forecast the movement of the assets traded on the Japanese stock market. In addition, the models are compared to a feed forward deep neural network, and it is found that all of the models consistently reach above 50% in directional bias forecasting for the stock market. The results of our study contribute to a better understanding of the complexity involved in stock market forecasting and give insight on the possible role that machine learning could play in this context. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.16855&r=fmk |
| By: | Joao Vitor Matos Goncalves; Michel Alexandre; Gilberto Tadeu Lima |
| Abstract: | This paper assesses the impact of time horizon on the relative performance of traditional econometric models and machine learning models in forecasting stock market prices. We employ an extensive daily series of Brazil IBX50 closing prices between 2012 and 2022 to compare the performance of two forecasting models: ARIMA (autoregressive integrated moving average) and LSTM (long short-term memory) models. Our results suggest that the ARIMA model predicts better data points that are closer to the training data, as it loses predictive power as the forecast window increases. We also find that the LSTM model is a more reliable source of prediction when dealing with longer forecast windows, yielding good results in all the windows tested in this paper. |
| Keywords: | Finance; machine learning; deep learning; stock market |
| JEL: | C22 C45 C53 G17 |
| Date: | 2023–11–17 |
| URL: | http://d.repec.org/n?u=RePEc:spa:wpaper:2023wpecon13&r=fmk |
| By: | Kang Gao; Stephen Weston; Perukrishnen Vytelingum; Namid R. Stillman; Wayne Luk; Ce Guo |
| Abstract: | We propose the Chiarella-Heston model, a new agent-based model for improving the effectiveness of deep hedging strategies. This model includes momentum traders, fundamental traders, and volatility traders. The volatility traders participate in the market by innovatively following a Heston-style volatility signal. The proposed model generalises both the extended Chiarella model and the Heston stochastic volatility model, and is calibrated to reproduce as many empirical stylized facts as possible. According to the stylised facts distance metric, the proposed model is able to reproduce more realistic financial time series than three baseline models: the extended Chiarella model, the Heston model, and the Geometric Brownian Motion. The proposed model is further validated by the Generalized Subtracted L-divergence metric. With the proposed Chiarella-Heston model, we generate a training dataset to train a deep hedging agent for optimal hedging strategies under various transaction cost levels. The deep hedging agent employs the Deep Deterministic Policy Gradient algorithm and is trained to maximize profits and minimize risks. Our testing results reveal that the deep hedging agent, trained with data generated by our proposed model, outperforms the baseline in most transaction cost levels. Furthermore, the testing process, which is conducted using empirical data, demonstrates the effective performance of the trained deep hedging agent in a realistic trading environment. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.18755&r=fmk |
| By: | Peter Albrecht; Evžen Kočenda; Evžen Kocenda |
| Abstract: | We provide a comprehensive assessment of volatility connectedness between the currencies of Central European (CE) countries using high-frequency data from 2009 to 2022. We assess asymmetries in connectedness (not investigated for CE currencies before) and document domination of the negative volatility, especially during periods of economic distress. We further bring the first statistical evidence based on a formal bootstrap-after-bootstrap procedure of Greenwood-Nimmo et al. (2023) that increases in connectedness are linked with systematic events, and identify the impact of specific domestic and global shocks. We find that for eight out of eight endogenously selected global events, there was an increase in connectedness within a maximum of one business month from the event's occurrence. Finally, we show that the connectedness is linked with its potential drivers: uncertainty, liquidity, and economic activity whose impacts differ substantially. Our results are robust with respect to a volatility measure and provide direct policy implications for portfolio composition and hedging. |
| Keywords: | volatility connectedness, Central European currencies, asymmetries in volatility connectedness, bootstrap-after-bootstrap procedure, portfolio composition and hedging |
| JEL: | C58 F31 F65 G01 G15 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10728&r=fmk |