Machine learning for financial transaction classification across companies using character-level word embeddings of text fields

An important initial step in accounting is mapping financial transfers to the corresponding accounts. We devised machine-learning-based systems that automate this process. They use word embeddings with character-level features to process transaction texts. When considering 473 companies independently, our approach achieved an average top-1 accuracy of 80.50%, outperforming baselines that exclude the transaction texts or rely on a lexical bag-of-words text representation. We extended the approach to generalizes across companies and even across different corporate sectors. After standardization of the account structures and careful feature engineering, a single classifier trained on 44 companies from 28 sectors achieved a test accuracy of more than 80%. When trained on 43 companies and tested on the remaining one, the system achieved an average performance of 64.62%. This rate increased to nearly 70% when considering only the largest sector.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

FDI facilitated by agglomeration economies: evidence from manufacturing and services joint ventures in China

The open door policy of China’s economic reform since the 1980s has attracted heavy foreign direct investment (FDI) flows into China and especially to Guangdong (particularly the Pearl River Delta region, PRD) and induced significant economic growth during the past two decades. While there exist various classical theories of FDI in attempting to identify the determinants of FDI inflow and to explain the behavior of FDI flows, limited attention has been given from the perspective of agglomeration effects generated by a core-periphery (CP) relation.This paper intends to study the impacts of agglomerations on FDI inflows in the context of Krugman’s CP relation (1991) by investigating (1) the formation of a CP relation via gravity model analysis; (2) whether different types of industry FDI flows will respond differently in the CP-system, given agglomeration effects; and (3) whether FDI origin and firm scale matter in affecting FDI flows.A database consisting of a population frame of 37,742 firm-level manufacturing and services joint ventures investing in Guangdong in 1998 was used. Empirical results show that the agglomerations of the CP relation have affected FDI flow patterns. While both manufacturing and services FDI and sources of investment responded differently to the impacts, smaller firms were found more responsive to the CP-agglomeration settings regardless of FDI by industry type and by source. The significance and implications of the CP-system to further facilitate FDI in the region are discussed.     

Do interventions in foreign exchange markets modify investors' expectations? The experience of Japan between 1992 and 2004

The purpose of this paper is to analyze the impact of the Bank of Japan's official interventions on the JPY/USD parity during the period 1992–2004. The novelty of our approach is to combine two recent advances of the empirical literature on foreign exchange interventions: (i) drawing on over-the-counter option prices to characterize more precisely the distribution of market expectations; (ii) redefining interventions in terms of events as they tend to come in clusters. Moreover, in order to deal with the features of the data (small sample size, non-standard distribution), we use bootstrap tests.We show that interventions have a significant impact on the mean expectation (the forward rate). The results are more ambiguous for variance. Additionally, we find that the effect of interventions on skewness is significant, robust to different definitions of skewness, and consistent with the direction of interventions. On the contrary, our results clearly show that kurtosis is not affected by interventions. We finally show that: (i) coordination increases effectiveness of interventions; (ii) results are not altered when controlling for other economic and political news.     

How to measure mutual fund performance: economic versus statistical relevance

In the present paper a comprehensive assessment of existing mutual fund performance models is presented. Using a survivor‐bias free database of all US mutual funds, we explore the added value of introducing extra variables such as size, book‐to‐market, momentum and a bond index. In addition to that we evaluate the use of introducing time‐variation in betas and alpha. The search for the most suitable model to measure mutual fund performance will be addressed along two lines. First, we are interested in the statistical significance of adding more factors to the single factor model. Second, we focus on the economic importance of more elaborate model specifications. The added value of the present study lies both in the step‐wise process of identifying relevant factors, and the use of a rich US mutual fund database that was recently released by the Center for Research in Security Prices.     

Equity Volatility and Corporate Bond Yields

This paper explores the effect of equity volatility on corporate bond yields. Panel data for the late 1990s show that idiosyncratic firm‐level volatility can explain as much cross‐sectional variation in yields as can credit ratings. This finding, together with the upward trend in idiosyncratic equity volatility documented by Campbell, Lettau, Malkiel, and Xu (2001), helps to explain recent increases in corporate bond yields.     

The impacts of Hong Kong’s Currency Board reforms on the interbank market

Among the economies with a Currency Board System (CBS), Hong Kong (HK) is probably the one with the largest and most developed financial sector, as well as the highest capital mobility. Hence, studying HK’s CBS is not only crucial to HK, but also important for the understanding of the modern CBS. This paper outlines the major monetary reforms in HK since the late 1980s. The impacts of these reforms and the 1997–1998 Asian Financial Crisis are then examined empirically. We focus on the differentials between the US and HK interbank interest rates. We assume the conditional-mean equation follows an autoregressive process and the conditional-variance equation follows a generalized autoregressive conditional heteroscedasticity process. This model captures the time-varying level and volatility of the differential. In light of the empirical results we provide an assessment of the reforms in HK.