环境规制、融资约束与企业污染减排——来自排污费标准调整的证据

“十四五”规划提出建设人与自然和谐共生的现代化,这要求持续推进污染减排促进经济绿色低碳转型,实现环境与经济协同发展。基于2007年起排污费提高的政策冲击和2004-2013年工业企业污染数据,本文使用倍差法考察排污费提高的污染减排效果以及融资约束对政策效应的影响。研究发现排污费提高后,污染排放水平显著下降,但产出也受到较大冲击;企业减排方式存在明显差异,大型企业主要通过降低污染强度的方式来降低污染排放,而中小型企业则主要采取降低生产规模的方式来降低污染排放;进一步基于环境投融资角度对企业减排行为的分析揭示,融资约束影响中小企业污染减排,加剧排污费提高对产出的影响。因此,提高绿色金融的环境投融资供给能力是促进经济绿色转型的重要途径。     

Trading activity and price discovery in Bitcoin futures markets

This study examines the impact of trading activities on price discovery in the Bitcoin futures markets. We find that trades of hedgers are positively correlated with the modified information shares in both CME and CBOE futures markets, suggesting that their trading promotes futures market efficiency. Retailers’ trading activity relates negatively to the price discovery of the CME Bitcoin futures and thus destabilizes the market. Speculators exert positive (negative) impact on the price discovery in the CME (CBOE) Bitcoin futures. Our finding that CME’s Bitcoin futures exhibit superior price discovery than CBOE’s provides plausible justification for CBOE’s decision in March 2019 to suspend further listings of Bitcoin futures contracts.     

Data vs. information: Using clustering techniques to enhance stock returns forecasting

This paper explores the use of clustering models of stocks to improve both (a) the prediction of stock prices and (b) the returns of trading algorithms.We cluster stocks using k-means and several alternative distance metrics, using as features quarterly financial ratios, prices and daily returns. Then, for each cluster, we train ARIMA and LSTM forecasting models to predict the daily price of each stock in the cluster. Finally, we employ the clustering-empowered forecasting models to analyze the returns of different trading algorithms.We obtain three key results: (i) LSTM models outperform ARIMA and benchmark models, obtaining positive investment returns in several scenarios; (ii) forecasting is improved by using the additional information provided by the clustering methods, therefore selecting relevant data is an important preprocessing task in the forecasting process; (iii) using information from the whole sample of stocks deteriorates the forecasting ability of LSTM models.These results have been validated using data of 240 companies of the Russell 3000 index spanning 2017 to 2022, training and testing with different subperiods.     

股价信息含量与投资-股价敏感性——基于融资融券的准自然实验

本文利用2006-2015年间的数据研究了融资融券对投资-股价敏感性的影响。利用融资融券作为股价信息含量的一个外生冲击变量,本文研究发现,我国A股市场确实存在反馈效应,融资融券政策的实施增强了标的公司投资-股价敏感性,这个结论在采用倾向得分模型(PSM)配对后依然成立,说明融资融券使股价融入了更多有利于管理层投资决策的信息。其次,融资融券对投资-股价敏感性的影响在机构投资者比例高、流动性高、处于新兴行业的这类管理层反馈效应更强的股票组中更显著。参照以往研究考虑了融资约束对反馈效应的调节作用,同样发现融资融券对投资-股价敏感性的影响在国有企业和规模较大的企业组中更显著。最后,融资融券交易规模越大,投资对股价的敏感性越强。     

Combining wavelet decomposition with machine learning to forecast gold returns

This paper combines the discrete wavelet transform with support vector regression for forecasting gold-price dynamics. The advantages of this approach are investigated using a relatively small set of economic and financial predictors. I measure model performance by differentiating between a statistically-motivated out-of-sample forecasting exercise and an economically-motivated trading strategy. Disentangling the predictors with respect to their time and frequency domains leads to improved forecasting performance. The results are robust compared to alternative forecasting approaches. My findings on the relative importances of such wavelet decompositions suggest that the influences of short-term and long-term trends are not stable over the full evaluation period.     

Classification of intraday S&P500 returns with a Random Forest

Stock markets can be interpreted to a certain extent as prediction markets, since they can incorporate and represent the different opinions of investors who disagree on the implications of the available information on past and expected events and trade on their beliefs in order to achieve profits. Many forecast models have been developed for predicting the future state of stock markets, with the aim of using this knowledge in a trading strategy. This paper interprets the classification of the S&P500 open-to-close returns as a four-class problem. We compare four trading strategies based on a random forest classifier to a buy-and-hold strategy. The results show that predicting the classes with higher absolute returns, ‘strong positive’ and ‘strong negative’, contributed the most to the trading strategies on average. This finding can help shed light on the way in which using additional event outcomes for the classification beyond a simple upward or downward movement can potentially improve a trading strategy.     

Size and information revelation in securities trading

We examine differences in information content between order submission sizes and trade sizes by U.S. equity traders. Increasing (decreasing) order submission (trade) size is reflective of information. The result suggests that better-informed traders want to trade in a large size, but that they engage in stealth trading practices or break larger orders into smaller sizes in order to conceal information. While prior studies tend to narrowly focus on trade executions at the market-centre level, our findings indicate that order submission size varies significantly from trade size and that both sizes are informative about future prices, albeit in an inverse manner.     

Price gap anomaly in the US stock market: The whole story

This paper analyses the price gap anomaly in the US stock market (comprised of the DJI, S&P 500 and NASDAQ) covering the period 1928 to 2018. This paper aims to investigate whether or not price gaps create market inefficiencies. Price gaps occur when the current day’s opening price is different from the previous day’s closing price due orders placed before the opening of the market. Several hypotheses are tested using various statistical tests (Student’s t-test, ANOVA, Mann-Whitney test), regression analysis, and special methods, that is, the modified cumulative returns and the trading simulation approaches. We find strong evidence in favour of abnormal price movements after price gaps. We observe that during a gap day prices tend to change in the direction of the gap. A trading strategy based on this anomaly was efficient in that its results were not random, indicating that this market was not efficient. The momentum effect was found to be temporary and no evidence of seasonality in price gaps was found. Lastly, our results were also contrary to the myth that price gaps tend to get filled.     

When do retail investors pay attention to their trading platforms?

Buying and selling securities through online trading platforms has become increasingly popular among U.S. households in recent years. This study tracks U.S. households' attention to their online trading platforms using daily data for 2004 to August 2017. The analysis covers the 10 most popular online trading platforms among U.S. investors. The findings indicate that market shocks, captured by several proxies, as well as macroeconomic announcements attract investors' attention to trading platforms. We also document that the ostrich effect weakens when considering greater changes in the VIX. Our findings do not support the avoidance of information theory, but do support the theoretical argument that risk-averse agents engage in more information gathering when uncertainty prevails in hopes of reducing their risks.     

High frequency momentum trading with cryptocurrencies

Over the past few years, cryptocurrencies have increasingly been discussed as alternatives to traditional fiat currencies. These digital currencies have garnered significant interest from investment banks and portfolio managers as a potential option to diversify the financial risk from investing in other assets. This interest has also extended to the general public who have seen cryptocurrencies as a way of making a quick profit. This paper provides a first insight into the applicability of high frequency momentum trading strategies for cryptocurrencies. We implemented two variations of a signal-based momentum trading strategy: (i) a time series method; (ii) a cross sectional method. These strategies were tested on a selection of seven of the largest cryptocurrencies ranked by market capitalization. The results show that there exists potential for the momentum strategy to be used successfully for cryptocurrency trading in a high frequency setting. A comparison with a passive portfolio strategy is proposed, which shows abnormal returns when compared with the momentum strategies. Furthermore, the robustness of our results are checked through the application of the momentum strategies other sample periods. We also compare the performances of the signal-based momentum strategies with returns-based versions of the strategies. It is shown that the signal-based strategy outperforms the returns-based strategy. However, there appears to be no single parameterization of the signal-based strategies that can generate the greatest cumulative return over all sample periods.