Futures Manipulation with “Cash Settlement”

This paper investigates the susceptibility of futures markets to price manipulation in a two-period model with asymmetric information and “cash settlement” futures contracts. Without “physical delivery,” strategies based on “corners” or “squeezes” are infeasible. However, uninformed investors still earn positive expected profits by establishing a futures position and then trading in the spot market to manipulate the spot price used to compute the cash settlement at delivery. We also show that as the number of manipulators grows, profits from manipulation fall to zero. However, even in the limit, manipulation still has a nontrivial impact on market liquidity. More broadly, we interpret manipulation as a form of endogenous “noise trading” which can arise in multiperiod security markets.     

Arbitraging Arbitrageurs

This paper develops a theory of strategic trading in markets with large arbitrageurs. If arbitrageurs are not well capitalized, capital constraints make their trades predictable. Other market participants can exploit this by trading against them. Competitors may find it optimal to lend to arbitrageurs that are financially fragile; additional capital makes the arbitrageurs more viable, and lenders can reap profits from trading against them for a longer time. The strategic behavior of these market participants has implications for the functioning of financial markets. Strategic trading may produce significant price distortions, increase price manipulation, and trigger forced liquidations of large traders.     

The influence of cultural factors on price clustering: Evidence from Asia–Pacific stock markets

Price clustering is the tendency of prices to be observed more frequently at some numbers than others. It increases with haziness, or imprecision, about underlying value. Most research on price clustering has been conducted in Western financial markets, where there is manifest preference for trading at round numbers.We focus on number preferences under Chinese culture. Many Chinese believe some numbers are “unlucky” and to be avoided. For instance, the number 4 is inauspicious because the Cantonese pronunciation of 4 is similar to the phrase “to die”. We first document clustering of daily closing prices on six Asia–Pacific stock markets, three with predominantly Chinese populations. Next, we fit binomial logit models within these markets to estimate the association between structural and economic factors, and culture, on price clustering. We find some support for the influence of Chinese culture and superstition on year-round number preferences of traders, but it is located solely in the Hong Kong market. Furthermore, in the Hong Kong market Chinese culture and superstition help explain the increased avoidance of the number 4 during the auspicious Chinese New Year, Dragon Boat and Mid-Autumn festivals.     

Convex duality in optimal investment and contingent claim valuation in illiquid markets

This paper develops duality theory for optimal investment and contingent claim valuation in markets where traded assets may be subject to nonlinear trading costs and portfolio constraints. Under fairly general conditions, the dual expressions decompose into three terms, corresponding to the agent’s risk preferences, trading costs and portfolio constraints, respectively. The dual representations are shown to be valid when the market model satisfies an appropriate generalization of the no-arbitrage condition and the agent’s utility function satisfies an appropriate generalization of asymptotic elasticity conditions. When applied to classical liquid market models or models with bid–ask spreads, we recover well-known pricing formulas in terms of martingale measures and consistent price systems. Building on the general theory of convex stochastic optimization, we also obtain optimality conditions in terms of an extended notion of a “shadow price”. The results are illustrated by establishing the existence of solutions and optimality conditions for the nonlinear market models recently proposed in the literature. Our results allow significant extensions including nondifferentiable trading costs which arise, e.g., in modern limit order markets where the marginal price curve is necessarily discontinuous.     

GAMBLING, SPECULATION, AND INSURANCE: WHY THEY CONTINUE TO BE CONFUSED AND CONDEMNED

Governments and the media have often attacked financiers for “speculating” in their countries' currencies, thereby forcing them to make drastic and sometimes painful changes in monetary and fiscal policies. This article argues that such accusations have no basis in economic theory, and that “such rhetoric should be seen for what it is: an attempt by politicians and policy-makers to divert attention and blame from their own mismanagement.” More generally, the author argues that the failure of the general public to understand the social benefits of financial activies such as trading in government bonds, commodity futures, and, more recently, financial derivatives has led throughout history to “prejudice, bad laws, and bad regulations.” Much as the charging of interest and certain forms of insurance were proscribed by the medieval Church, agricultural commodity futures were attacked in the 19th century (and in much of the 20th as well) in the U.S. and elsewhere as thinly disguised forms of gambling. Moreover, the same restrictions that were imposed on gambling and futures markets during the 19th and early 20th centuries are now imposed in many Third-World countries. Instead of encouraging the use of forward markets by small producers and traders, and promoting the development of organized commodity markets and banks in local centers, most less-developed countries today support national and international “stabilization” measures such as buffer stocks and regulations like price floors, price ceilings, and crop quotas. Meanwhile, in Western nations, governments continue to accuse financial markets of “destabilizing speculation” and of a myopic obsession with short-term profitability—even as the U.S. IPO market continues to assign record values to companies that have yet to show profits. Viewed in this light, the media and regulatory assaults on the junk bond markets in the late 1980s and on derivatives in the early 1990s are only the latest in a long line of misguided attacks on financial innovation.     

Legal insider trading and market efficiency

Does legal insider trading contribute to market efficiency? Using refinements proposed in the recent microstructure literature, we analyzed the information content of legal insider trading. We used data on 2110 companies subject to 59,244 aggregated daily insider trades between January 1995 and the end of September 1999. Our main finding is that, even though financial markets do not respond strongly in terms of abnormal returns to insider trading activities, the significant change in price sensitivity to relative order imbalance due to abnormal insider trades reveals that price discovery is hastened on insider trading days.     

Statement of the Financial Economists Roundtable* Reforming the OTC Derivatives Markets

In investigations of the causes of the crisis, a major focus has been the role of derivative securities, particularly credit-default swaps (CDS). Despite widespread claims to the contrary, however, the 51 economists who signed this statement begin by asserting that CDS and other derivatives contracts were not a primary cause of the financial crisis. At the same time, derivatives markets are said to play an important economic role by shifting risks from businesses and individual investors to parties more willing (and generally better able) to bear them. But, as illustrated during the crisis, derivatives also can be used to transmit risk in ways that have the potential to pervade the entire financial system. With the aim of limiting systemic risk associated with the use of derivatives, the statement recommends the following:

• • measures that encourage migration of more derivatives transactions to central-clearing facilities, including higher capital requirements and stricter criteria (including segregation) for the collateralization of positions that are not cleared;
• • data reporting and repository requirements designed to help regulators and market participants to understand systemic risk exposures in the financial system;
• • post-trade price transparency for all sufficiently standardized OTC products;
• • continued migration of trading in actively traded OTC products to exchanges.

Finally, although the economists support regulations against market manipulation, they oppose potential restrictions on speculative trading, including the holding of “naked” CDS, while affirming that both hedging and speculation are important and socially beneficial activities in our financial system.     

Macroeconomic Implications of “Deep Habits” in Banking

Recent empirical evidence shows that price‐cost margins in the market for bank credit are countercyclical in the U.S. economy and that this cyclical behavior can be explained in part from the fact that switching banks is costly for customers (i.e., from a borrower hold‐up effect). Our goal, in this paper, is to study the “financial accelerator” role of these countercyclical margins as a propagation mechanism of macroeconomic shocks. To do so, we apply the “deep habits” framework in Ravn, Schmitt‐Grohé, and Uribe (2006) to financial markets to model this hold‐up effect within a monopolistically competitive banking industry. We are able to reproduce the pattern of price‐cost margins observed in the data, and to show that the real effects of aggregate total factor productivity shocks are larger the stronger the friction implied by borrower hold‐up. Also, output, investment, and employment all become more volatile than in a standard model with constant margins in credit markets. An empirical contribution of our work is to provide structural estimates of the deep habits parameters for financial markets.     

Arbitrage and deflators in illiquid markets

This paper presents a stochastic model for discrete-time trading in financial markets where trading costs are given by convex cost functions and portfolios are constrained by convex sets. The model does not assume the existence of a cash account/numeraire. In addition to classical frictionless markets and markets with transaction costs or bid–ask spreads, our framework covers markets with nonlinear illiquidity effects for large instantaneous trades. In the presence of nonlinearities, the classical notion of arbitrage turns out to have two equally meaningful generalizations, a marginal and a scalable one. We study their relations to state price deflators by analyzing two auxiliary market models describing the local and global behavior of the cost functions and constraints.     

Algorithmic complexity of financial motions

We survey the main applications of algorithmic (Kolmogorov) complexity to the problem of price dynamics in financial markets. We stress the differences between these works and put forward a general algorithmic framework in order to highlight its potential for financial data analysis. This framework is “general” in the sense that it is not constructed on the common assumption that price variations are predominantly stochastic in nature.     

A pairs trading strategy based on linear state space models and the Kalman filter

Among many strategies for financial trading, pairs trading has played an important role in practical and academic frameworks. Loosely speaking, it involves a statistical arbitrage tool for identifying and exploiting the inefficiencies of two long-term, related financial assets. When a significant deviation from this equilibrium is observed, a profit might result. In this paper, we propose a pairs trading strategy entirely based on linear state space models designed for modelling the spread formed with a pair of assets. Once an adequate state space model for the spread is estimated, we use the Kalman filter to calculate conditional probabilities that the spread will return to its long-term mean. The strategy is activated upon large values of these conditional probabilities: the spread is bought or sold accordingly. Two applications with real data from the US and Brazilian markets are offered, and even though they probably rely on limited evidence, they already indicate that a very basic portfolio consisting of a sole spread outperforms some of the main market benchmarks.     

An empirical investigation of large trader market manipulation in derivatives markets

Using account-level transaction data in options and futures markets, we investigate the existence of market manipulation, which is the ability of large traders to trade strategically, impacting prices and making abnormal profits. First, large trader’s option positions have a quantity impact on the underlying asset’s price. Second, large traders generate significantly positive alphas from trading options and futures. Among the different investor types, proprietary dealers generate the largest positive alphas. Third, these abnormal returns are consistent with strategic trading and cross-market manipulation. The evidence supports market manipulation across the options and futures markets, but not within the futures market itself.     

Opening price manipulation and its value influences

We investigate how opening price manipulation influences market behaviors and investors' returns. Analyzing direct evidence comprising 87 opening price manipulation cases, and indirect evidence consisting of 19,003 suspected cases detected by an opening price manipulation identification model that we construct, we examine the impact of manipulation on mispricing, investors' welfare, trading activity and price volatility. Our results indicate that manipulated stocks experience significantly lower returns and a higher probability of price reversal after manipulation. Investors who purchase manipulated stocks at their opening price, or the volume-weighted average price, on the manipulation day make losses on their investments. Further, manipulation increases market trading activity and price volatility due to the influx of retail investors. Our additional analysis demonstrates that enhancing the intensity of external supervision and internal governance can mitigate mispricing caused by opening price manipulation. Our study provides novel evidence of the economic consequences of open market manipulation and policy implications for governments and regulators to develop effective supervisory processes to reduce manipulation and mitigate its impact on efficient markets.     

Two Paradigms for The Market Value of Liabilities

Abstract

Asset/liability management (ALM) theory and practices of insurers have matured and developed from early applications to guaranteed investment contracts (GICs) to all annuity and insurance products today. An important and logical next step of inquiry is the definition of, and calculation procedures for, the market value of an insurance liability. Because all ALM strategies have as their goal the management of some value of assets in relation to some value of liabilities, this inquiry will provide at last a canonical basis for ALM: the management of relative market values.

To set the stage for this exploration, the theory and application of pricing in a complete market are reviewed, as are the practical limitations of this theory in the real, and far from complete, financial markets. The notion of an ad hoc pricing model is developed, and examples are reviewed and critiqued. These models, though imperfect compared with pricing in a complete market, bridge the gap between pricing theory and practice.

The current state of the liabilities market is also discussed, and this market is seen to naturally split into a “long” and a “short” submarket. Of particular interest is the theoretical possibility of these markets becoming broad-based, deep and active, and the conclusions are relevant to the issue of long/short price equalization.

Two paradigms are then explored for defining and subsequently calculating an insurance liability market value. A “paradigm” is a generalized model or framework for accomplishing the task at hand. Each paradigm reflects observable market trading activity, however infrequent, and each is based on methods of valuation consistent with finance-theoretic approaches that are routinely used for the market valuation of assets.

In addition, each paradigm allows for a sequence of ad hoc valuation methodologies, which differ in the extent to which various risks are explicitly modeled versus judgmentally reflected in a risk spread. These paradigms are discussed and contrasted, and arguments made for the potential evolution of the respective values if a “liability” market began trading actively. Practical constraints on the realization of this evolution are also noted.

The last section of this paper discusses a host of considerations related to the application of option-pricing theory to insurance company liabilities.     

Futures volatility forecasting based on big data analytics with incorporating an order imbalance effect

Future markets play vital roles in supporting economic activities in modern society. For example, crude oil and electricity futures markets have heavy effects on a nation’s energy operation management. Thus, volatility forecasting of the futures market is an emerging but increasingly influential field of financial research. In this paper, we adopt big data analytics, called Extreme Gradient Boosting (XGBoost) from computer science, in an attempt to improve the forecasting accuracy of futures volatility and to demonstrate the application of big data analytics in the financial spectrum in terms of volatility forecasting. We further unveil that order imbalance estimation might incorporate abundant information to reflect price jumps and other trading information in the futures market. Including order imbalance information helps our model capture underpinned market rules such as supply and demand, which lightens the information loss during the model formation. Our empirical results suggest that the volatility forecasting accuracy of the XGBoost method considerably beats the GARCH-jump and HAR-jump models in both crude oil futures market and electricity futures market. Our results could also produce plentiful research implications for both policy makers and energy futures market participants.     

REVISITING MARKET EFFICIENCY: THE STOCK MARKET AS A COMPLEX ADAPTIVE SYSTEM

Well‐functioning financial markets are key to efficient resource allocation in a capitalist economy. While many managers express reservations about the accuracy of stock prices, most academics and practitioners agree that markets are efficient by some reasonable operational criterion. But if standard capital markets theory provides reasonably good predictions under “normal” circumstances, researchers have also discovered a number of “anomalies”—cases where the empirical data appear sharply at odds with the theory. Most notable are the occasional bursts of extreme stock price volatility (including the recent boom‐and‐bust cycle in the NASDAQ) and the limited success of the Capital Asset Pricing Model in accounting for the actual risk‐return behavior of stocks. This article addresses the question of how the market's efficiency arises. The central message is that managers can better understand markets as a complex adaptive system. Such systems start with a “heterogeneous” group of investors, whose interaction leads to “self‐organization” into groups with different investment styles. In contrast to market efficiency, where “marginal” investors are all assumed to be rational and well‐informed, the interaction of investors with different “decision rules” in a complex adaptive system creates a market that has properties and characteristics distinct from the individuals it comprises. For example, simulations of the behavior of complex adaptive systems suggest that, in most cases, the collective market will prove to be smarter than the average investor. But, on occasion, herding behavior by investors leads to “imbalances”—and, hence, to events like the crash of '87 and the recent plunge in the NASDAQ. In addition to its grounding in more realistic assumptions about the behavior of individual investors, the new model of complex adaptive systems offers predictions that are in some respects more consistent with empirical findings. Most important, the new model accommodates larger‐than‐normal stock price volatility (in statistician's terms, “fat‐tailed” distributions of prices) far more readily than standard efficient market theory. And to the extent that it does a better job of explaining volatility, this new model of investor behavior is likely to have implications for two key areas of corporate financial practice: risk management and investor relations. But even so, the new model leaves one of the main premises of modern finance theory largely intact–that the most reliable basis for valuing a company's stock is its discounted cash flow.     

How effective are trading pauses?

Exploiting Nasdaq order book data and difference-in-differences methodology, we identify the distinct effects of trading pause mechanisms introduced on US stock exchanges after May 2010. We show that the mere existence of such a regulation makes market participants behave differently in anticipation of a pause. Pauses enhance price discovery during the break but have adverse effects on price stability and liquidity after the pause. We find that pauses ultimately do not “cool off” markets but cause extra volatility. This implies a regulatory trade-off between the protective role of trading pauses and their adverse effects on market quality.     

Locals,foreigners, and multi-market trading of equities: Intraday evidence from Thailand

We study stock market orders and trades in a developing country, Thailand, where foreign ownership limits partially segment local and foreign investors into two distinct markets. Some foreigners forgo voting rights and distributions to trade on the “local board”, while some locals forgo such benefits and pay a price premium to trade on the “foreign board”. Regardless of nationality, these cross-market traders typically submit orders when liquidity is high, fill orders at relatively beneficial prices, exploit patterns in stock prices across markets, display profitable holding-period returns, and enhance price discovery. This suggests that skilled, informed trading that affects market quality does not depend on trader nationality.     

What happened to liquidity when world war I shut the NYSE?

This paper examines how financial markets responded to the longest circuit breaker in American financial history: the four-month suspension of trading on the New York Stock Exchange following the outbreak of World War I. The suspension that began on July 31, 1914 fostered a substitute trading forum called the New Street market. Trading on New Street began almost immediately and offered economically meaningful liquidity services despite its impaired price transparency. A simple cross-sectional model of bid–ask spreads on New Street demonstrates that New Street liquidity responded to economic incentives. New Street's success implies that, from a public policy perspective, expensive back-up trading facilities are not required to preserve liquidity during a trading suspension in established markets. Back-up records of share ownership and transfer facilities, however, are crucial to maintaining liquidity.