Time to Slow Down for High-Frequency Trading? Lessons from Artificial Markets

In this paper, we focus on the French cancel order tax implemented on 1 August 2012. We question the effectiveness of the modified tax with no exemptions and we analyze its impact on market quality, measured by liquidity, volatility and efficiency. Additionally, this paper raises the question whether this tax leads to a reduction of high-frequency trading (HFT) activities and a decline in trading volume. Based on our findings we report that introduction of cancel order tax only slightly reduces HFT activities, but it significantly affects market liquidity, increases market volatility and leads to deteriorating market efficiency. We conclude that it is difficult to dissuade investors from entering into unproductive trades and eliminate negative outputs of HFT (such as price manipulations) through tax, without altering the benefits of HFT like liquidity provision and efficient price discovery.     

Prices or Knowledge? What Drives Demand for Financial Services in Emerging Markets?

Financial development is critical for growth, but its microdeterminants are not well understood. We test leading theories of low demand for financial services in emerging markets, combining novel survey evidence from Indonesia and India with a field experiment. We find a strong correlation between financial literacy and behavior. However, a financial education program has modest effects, increasing demand for bank accounts only for those with limited education or financial literacy. In contrast, small subsidies greatly increase demand. A follow‐up survey confirms these findings, demonstrating that newly opened accounts remain open and in use 2 years after the intervention.     

Financial Institutions and Non‐Governmental Organizations: An Advocacy Partnership for Sustainable Capital Markets?

In this article, the Chief Responsibility Officer for Aviva Investors examines the potential for financial institutions (FIs) to work in partnership with non‐governmental organizations (NGOs) in advocating changes to public policy that promote sustainable capital markets. Many NGOs have argued that the current form of global economic growth is unsustainable—and they routinely engage in public policy advocacy. However, such advocacy has generally proved ineffective, in large part because most NGOs have a very limited understanding of how capital markets function. Investors, for their part, are increasingly recognizing that key aspects of the global economy are on an unsustainable footing. And some are concerned about the negative implications for the long‐term value of their assets. But with a few notable exceptions, they have not made systematic efforts to work with governments to correct the market failures. NGO‐FI advocacy partnerships could identify specific cases of systemic or sectoral market failures, and recommend long‐term changes to the sectoral operating environment that would affect the cash flows and values of companies operating within that sector in ways designed to “internalize” the effects of negative social and environmental externalities. To foster the development of such partnerships, there is a need for academia to develop learning forums that stimulate the exchange of ideas between the executives within NGOs and FIs in an environment of mutual trust and respect.     

What Makes a Better Annuity?

Given costly and limited annuity products, we investigate how annuity market innovation could improve participation and increase individual welfare. We find that participation gains are most likely with new annuity products that concentrate on late‐life payouts. Our welfare analysis suggests that annuity innovation should focus on adding survival contingencies to assets commonly held by individuals. Finally, in a complete market setting, we find demand only for those annuity contracts with a significant time gap between purchase and payout (a rarity in current contracts). Overall, our analysis indicates ample opportunity for innovation to spur annuity demand and improve individual welfare.     

Does Trading Volume Contain Information to Predict Stock Returns? Evidence from China's Stock Markets

This paper examines empirical contemporaneous and causal relationships between trading volume, stock returns and return volatility in China's four stock exchanges and across these markets. We find that trading volume does not Granger-cause stock market returns on each of the markets. As for the cross-market causal relationship in China's stock markets, there is evidence of a feedback relationship in returns between Shanghai A and Shenzhen B stocks, and between Shanghai B and Shenzhen B stocks. Shanghai B return helps predict the return of Shenzhen A stocks. Shanghai A volume Granger-causes return of Shenzhen B. Shenzhen B volume helps predict the return of Shanghai B stocks. This paper also investigates the causal relationship among these three variables between China's stock markets and the US stock market and between China and Hong Kong. We find that US return helps predict returns of Shanghai A and Shanghai B stocks. US and Hong Kong volumes do not Granger-cause either return or volatility in China's stock markets. In short, information contained in returns, volatility, and volume from financial markets in the US and Hong Kong has very weak predictive power for Chinese financial market variables.     

What Are You Saying? Using topic to Detect Financial Misreporting

We use a machine learning technique to assess whether the thematic content of financial statement disclosures (labeled topic) is incrementally informative in predicting intentional misreporting. Using a Bayesian topic modeling algorithm, we determine and empirically quantify the topic content of a large collection of 10-K narratives spanning 1994 to 2012. We find that the algorithm produces a valid set of semantically meaningful topics that predict financial misreporting, based on samples of Securities and Exchange Commission (SEC) enforcement actions (Accounting and Auditing Enforcement Releases [AAERs]) and irregularities identified from financial restatements and 10-K filing amendments. Our out-of-sample tests indicate that topic significantly improves the detection of financial misreporting by as much as 59% when added to models based on commonly used financial and textual style variables. Furthermore, models that incorporate topic significantly outperform traditional models when detecting serious revenue recognition and core expense errors. Taken together, our results suggest that the topics discussed in annual report filings and the attention devoted to each topic are useful signals in detecting financial misreporting.     

Who Values Economist Forecasts? Evidence From Trading in Treasury Markets

While economic forecasting is ubiquitous within the industry, its role in the trading process has received little attention in the literature. We examine how economist forecasts are related to trading activity in the OTC treasury bond market at the participant level. Consistent with models of heterogeneous opinions, we show that the forecasting economists employing institution places a disproportionately large reliance on the forecast. There is pervasive evidence that this reliance is asymmetric. Only forecasts which imply a fall in future treasury bond prices are associated with an abnormal trading reaction consistent with the forecast. Reference dependence and loss aversion offer one possible explanation for this asymmetric trading response.     

Is the Virtual Integration of Financial Markets Beneficial in Emerging Markets? Evidence from MILA

This article analyzes whether the Latin American Integrated Market (MILA) has been beneficial for its participants. Using a dynamic conditional correlation (DCC) model proposed by Engle (2002), we found evidence that creating MILA increased the correlation levels in stock returns of member countries. Evidence indicates that this increase occurs mainly due to the increase in traded volume in the country with the least developed stock market—Peru.

In short, findings suggest that in an integration process such as MILA, as stock market members differ, in terms of stock market development, the markets will benefit from the integration. However, in the long term these benefits dissipate over time.     

What Drives the Owner‐Occupied and Rental Housing Markets? Evidence from an Estimated DSGE Model

Most dynamic stochastic general equilibrium (DSGE) models with a housing market do not explicitly include a rental market and assume a tight mapping between house prices and rents over the business cycle. However, rents are much smoother than house prices in the data. We match this feature of the data by adding both an owner‐occupied housing market and a rental market in a standard DSGE model. The intertemporal preference shock accounts for more than half of the variation in house prices and contributes to residential investment fluctuations through the liquidity constraint, and nominal rigidity in rental contracts captures the variation in the price‐rent ratio.     

What Is the Cost of Financial Flexibility? Theory and Evidence for Make‐Whole Call Provisions

Firms commonly incorporate make-whole call provisions in their newly issued debt, presumably to improve their ability to retire debt early if circumstances require. In return for increased financial flexibility, firms must compensate bondholders with additional (incremental) yield. To estimate theoretical incremental yields, we use and calibrate a structural model for a large sample of callable and noncallable US corporate bonds issued between 1995 and 2004. In a frictionless model where calls occur only when they are in-the-money, theoretical incremental yields average approximately 2 basis points (bp). In an extended model that incorporates taxes, transactions costs, and randomly occurring exogenous events requiring early bond retirement, incremental yields average approximately 5 bp. Empirical analysis, however, indicates that observed incremental yields are significantly greater than model-generated values, averaging between 13 and 24 bp. In the later years of our sample period, however, observed incremental yields begin to converge to model-generated values.     

How Does Cross‐Border Activity Affect EU Banking Markets?

Cross‐border activity in the EU is widely viewed as a necessary condition for the implementation of a single banking market and therefore as a positive factor for the enhancement of competition and cost performance in the region. In this paper, we analyse the relevance of this view by investigating whether cross‐border activity really promotes competition and cost efficiency in EU banking markets. We also consider the potential role of a bank's mode of entry by comparing existing domestic banks that foreign banks take over (mergers and acquisitions) with new branches created by foreign banks, often through subsidiaries (greenfield operations). We consider the impact of cross‐border banks on cost efficiency (measured by the stochastic frontier approach), profitability (assessed through return on assets) and competition (measured by the Lerner index). We find that greenfield banks enhance cost efficiency and competition, while mergers and acquisitions hamper competition and cost efficiency. Therefore, our results suggest that EU authorities should promote only greenfield banks rather than all cross‐border entries.     

Has Financial Regulation Been a Flop? (or How to Reform Dodd‐Frank)

Recent bank regulations have imposed large compliance costs on banks of all sizes, and have increased the costs of borrowing to both consumers and companies. But in this summary of his recent book, the author argues that the problems with banking system regulation go well beyond the excessive costs. Indeed, Dodd‐Frank and other post‐crisis regulatory reforms have failed to address the major shortcomings that produced the crisis of 2007–2009. Most importantly, excessive housing finance risk was not dealt with adequately, and is already on the rise again. And prudential standards for banks, while much stricter, remain unreliable in a severe economic downturn. After providing evidence of the shortcomings of major parts of the regulatory structure created after 2008, the author points to fundamental problems in the thinking underlying the post‐crisis regulatory changes that made those reforms unlikely to succeed. Moreover, he argues that the new processes of financial regulation have fostered many abuses of the rule of law, which have politicized regulation, adding to its cost and discrediting important supervisory and regulatory agencies. The author suggests that simpler, more effective, less costly, and more respectable approaches to regulation are possible. He offers a set of principles to guide reform and proposes over 20 specific actions that would enhance the effectiveness, reduce the costs, and improve the processes of bank regulation.     

Are Trade Size‐Based Inferences About Traders Reliable? Evidence from Institutional Earnings‐Related Trading

The use of observed transaction sizes to differentiate between “small” and “large” investor trading patterns is widespread. A significant concern in such studies is spurious effects attributable to misclassification of transactions, particularly those originating from large investors. Such effects can arise unintentionally, strategically, or endogenously. We examine comprehensive records of a sample of institutional investors (i.e., “large” traders), including their order sizes and overall position changes, to assess the degree to which such misclassifications give rise to spurious inferences about “small” and “large” investor trading activities. Our analysis shows that these institutions are heavily involved in small transaction activity. It also shows that they increase their order sizes substantially in announcement periods relative to nonannouncement periods, presumably as an endogenous response to earnings news. In the immediate earnings announcement period, transaction size‐based inferences about directional trading are quite misleading—producing spurious “small trader” effects and, more surprisingly, erroneous inferences about “large trader” activity.     

Do Industry‐Level Analyses Improve Forecasts of Financial Performance?

Prior research documents mean reversion in firm profitability and growth under the implicit assumption that profitability and growth of all firms revert to a common benchmark at the same rate. However, a large body of academic research suggests that there are systematic interindustry differences (e.g., industry barriers to entry) that differentially affect firm performance based on industry membership. We evaluate the relative forecast accuracy of mean reverting models at the industry and economywide levels and find that industry-specific models are generally more accurate in predicting firm growth but not profitability.     

Is Proprietary Trading Detrimental to Retail Investors?

We study the conflict of interest that arises when a universal bank conducts proprietary trading alongside its retail banking services. Our data set contains the stock holdings of every German bank and those of their corresponding retail clients. We investigate (i) whether banks sell stocks from their proprietary portfolios to their retail customers, (ii) whether those stocks subsequently underperform, and (iii) whether retail customers of banks engaging in proprietary trading earn lower portfolio returns than their peers. We present affirmative evidence for all three questions and conclude that proprietary trading can, in fact, be detrimental to retail investors.