Dynamic time series momentum of cryptocurrencies

This paper examines the momentum effect for twenty cryptocurrencies compared to the US stock market. For this purpose, we implement a dynamic modeling approach to define and test momentum periods that follow a formation period for interday and various intraday price levels. We find evidence that large proportions of the asset classes’ formation periods are followed by momentum periods, strongly supporting the momentum effect. In particular cryptocurrencies have significantly larger and longer momentum periods in all frequencies which we attribute to the lower derivability of their intrinsic value leading to a higher degree of noise traders in the market. A momentum trading strategy based on the identical approach outperforms a buy-hold strategy for both asset classes, while only cryptocurrencies have higher risk-adjusted returns and lower downside risks than a passive investment. We also find critical price levels during structural elements of the momentum period where the volatility shortly but intensively increases and consequently initiates a price impulse in the direction of the momentum.     

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.     

基于组态视角的众创空间创客集聚机制研究

以江苏省383家众创空间为样本，应用组态思维和QCA方法整合资源与服务两个层面的6个条件，探讨形成众创空间创客集聚差异的多重并发和复杂机理。结果发现：①众创空间高创客集聚存在服务主导型、场地-创客教育主导型、全要素型3个组态，其中，服务主导型、场地-创客教育主导型能深层次、强有力地影响众创空间高创客集聚；②众创空间非高创客集聚存在单纯地产式、单纯线下投资促进、单纯线上投资促进3种组态，且对众创空间非高创客集聚的影响基本相当；③资金资源不充足、场地资源不充足与人员服务不充足在解释非高创客集聚时具有替代作用。     

开放度组合策略与创新绩效类型匹配关系研究——吸收能力的调节效应

根据创新开放广度与深度，提出高广度-高深度、高广度-低深度、低广度-高深度与低广度-低深度4种组合策略。以吸收能力为调节变量，构建开放度组合策略与创新绩效关系模型，以我国三大行业201家企业为调查对象，采用多元线性回归分析方法进行实证检验。研究表明：实施高广度-高深度组合策略的企业倾向于追求突破式创新绩效，实施其它三类组合策略的企业倾向于追求渐进式创新绩效；吸收能力不仅能够积极调节高广度-高深度、高广度-低深度、低广度-高深度组合策略与突破式创新绩效的关系，而且能够积极调节3种组合策略与渐进式创新绩效的关系，但是无法调节低广度-低深度组合策略与突破式创新绩效、渐进式创新绩效的关系。     

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.     

Structures,strategizing, and contested territories: A structuration perspective on strategy development in employer branding

This study examines strategy development in employer branding based on structuration theory, focusing on the interplay between employer branding structures and the (inter)actions of employer branding strategists. To analyse this interplay, this paper draws upon a series of strategy meetings in an employer branding project of a multinational construction company. This study adds to the current debate on employer branding by advancing the understanding of the structural embeddedness of strategizing in employer branding. Furthermore, the study proposes that strategy development shifts employer branding engagements into contested territory due to various and contradictory structures strategists need to deal with.     

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.     

Auction theory adaptations for real life applications

We develop extensions to auction theory results that are useful in real life scenarios.1. Since valuations are generally positive we first develop approximations using the log-normal distribution. This would be useful for many finance related auction settings since asset prices are usually non-negative.2. We formulate a positive symmetric discrete distribution, which is likely to be followed by the total number of auction participants, and incorporate this into auction theory results.3. We develop extensions when the valuations of the bidders are interdependent and incorporate all the results developed into a final combined realistic setting.4. Our methods can be a practical tool for bidders and auction sellers to maximize their profits. The models developed here could be potentially useful for inventory estimation and for wholesale procurement of financial instruments and also non-financial commodities.All the propositions are new results and they refer to existing results which are stated as Lemmas.