On the efficacy of stop-loss rules in the presence of overnight gaps

A stop-loss rule is a risk management tool whereby the investor predefines some condition that, upon being triggered by market dynamics, implies the liquidation of her outstanding position. Such a tool is widely used by practitioners in financial markets with the hope of improving their investment performance by cutting losses and consolidating gains. We analyze in this work the performance of four popular implementations of stop-loss rules applied to asset prices whose returns are modeled with consideration of overnight gaps, that is, jumps from the closing price of one day to the opening price of the next trading day. In addition, our models include acute momentary price drops (flash crashes), which are often believed to erode the performance gains that might be derived from stop-loss rules. For this analysis we consider different models of asset returns: random walk, autoregressive and regime-switching models. In addition, we test the performance of the considered stop-loss rules in a non-parametric, data-driven framework based on the stationary bootstrap. As a general conclusion we find that, even when including overnight gaps and flash crashes in our price models, in rising markets stop-loss rules improve the expected risk-adjusted return according to most metrics, while improving absolute expected return in falling markets. Furthermore, we find that in general the simple fixed percentage stop-loss rule may be, in risk-adjusted terms, the most powerful among the popular rules that this work considers.     

Lottery stocks and stop-loss rules

We show that stop-loss rules increase the returns to investment in stocks with lottery features. These stocks typically have sporadic big gains and frequent small losses. However, stop-loss rules can reduce losses and allow investors to receive the gains from large price increases. We also highlight that sell signals of popular technical rules resemble stop-loss rules and are effective at increasing risk-adjusted returns for lottery stock. These rules could have helped investors avoid losses from major historical drawdowns, are particularly beneficial in declining markets, and are robust to the inclusion of transaction costs.     

S -Convex Extrema,Taylor-Type Expansions and Stochastic Approximations

The present work studies s -convex orders using a remarkable probabilistic generalization of Taylor's theorem obtained by Massey & Whitt (1993) and further discussed by Lin (1994). We propose two methods for approximating a given risk with known first moments by means of s -convex extremal distributions. The goodness of those approximations is explored using stop-loss distances. Several applications show the interest of this approach in actuarial sciences.     

Effects of Price Regulations and Dark Pools on Financial Market Stability: An Investigation by Multiagent Simulations

We built an artificial market model and investigated the impact of large erroneous orders on financial market price formations. Comparing the case of consented large erroneous orders in the short term with that of continuous small erroneous orders in the long term, if amounts of orders are the same, we found that the orders induced almost the same price fall range. We also analysed effects of price variation limits for erroneous orders and found that price variation limits that employ a limitation term shorter than the time erroneous orders exist effectively prevent large price fluctuations. We also investigated effects of up-tick rules, adopting the trigger method that the Japan Financial Services Agency adopted in November 2013. We also investigated whether dark pools that never provide any order books stabilize markets or not using the model including one lit market, which provides all order books to investors, and one dark pool. We found that markets become more stable as the dark pool is increasingly used. We also found that using the dark pool more reduces the market impacts. However, if other investors’ usage rates of dark pools become too large, investors must use the dark pool more than other investors to avoid market impacts. When a tick size of a lit market is larger, dark pools are more useful to avoid market impacts. These results suggest that dark pools stabilize markets when the usage rate is under some threshold and negatively affect the market when the usage rate is over that threshold. Our simulation results suggest the threshold might be much larger than the usage rate in present real financial markets. This study is the first to discuss whether or not several concrete and actually adoptable regulations, including those that have never been employed (e.g. price variation limits with various parameters), could prevent large fluctuations of market prices, including those beyond our experience, using artificial market simulations, and to discuss quantitatively how spreading of dark pools beyond our experience could affect market price formations using the artificial market simulations. In short, this study is the first study to comprehensively discuss how regulations and financial systems beyond our experience could affect price formations using the artificial market simulations. Copyright © 2015 John Wiley & Sons, Ltd.     

Depth improvement and adjusted price improvement on the New York stock exchange

Traditional price improvement improperly assesses large orders’ execution quality by ignoring additional liquidity depth-exceeding orders receive at the quoted price and viewing orders that “walk the book” as “disimproved”. Ignoring this additional liquidity is particularly problematic when assessing execution quality in markets with significant non-displayed liquidity. To correct this deficiency, we modify the price benchmark used to determine whether an order is price improved by making the benchmark a function of the order's size relative to the quoted depth. We document that the differences between conventional price improvement and our measure, adjusted price improvement, can be dramatic and show that the difference depends on trading volume, stock price, and volatility.     

Traders’ choice between limit and market orders: evidence from NYSE stocks

In this paper, we examine a trader's order choice between market and limit orders using a sample of orders submitted through NYSE SuperDot. We find that traders place more limit orders relative to market orders when: (1) the spread is large, (2) the order size is large, and (3) they expect high transitory price volatility. A rise in informational volatility appears neither to increase nor decrease the placement of limit orders. We also find that a rise in lagged price volatility decreases the size of spread, which is driven by the increase in the placement of limit orders.     

Optimal liquidation in dark pools

We consider a large trader liquidating a portfolio using a transparent trading venue with price impact and a dark pool with execution uncertainty. The optimal execution strategy uses both venues continuously, with dark pool orders over-/underrepresenting the portfolio size depending on return correlations; trading at the traditional venue is delayed depending on dark liquidity. Pushing up prices at the traditional venue while selling in the dark pool might generate profits. If future returns depend on historical dark pool liquidity, then sending orders to the dark pool can be worthwhile simply to gather information.     

Distortion Risk Measures and Economic Capital

Abstract

To provide incentive for active risk management, it is argued that a sound coherent distortion risk measure should preserve some higher degree stop-loss orders, at least the degree-three convex order. Such risk measures are called tail-preserving risk measures. It is shown that, under some common axioms and other plausible conditions, a tail-preserving coherent distortion risk measure identifies necessarily with the Wang right-tail measure or the expected value measure. This main result is applied to derive an optimal economic capital formula.     

Modeling Frost Losses: Application to Pricing Frost Insurance

The main objective of this article is to model the losses caused by frost events and use it to price frost insurance. Since the data on frost events are either unavailable or rarely available, we have chosen to obtain a model for frost losses based on temperature by using some fundamental agricultural engineering findings on frost damage. The main challenges in modeling frost loss variables are, first, the nonlinearity of the frost losses with respect to the temperature and, second, the fruit resistance to the first few hours of low temperature. We address both issues when introducing our frost loss variable. Then after finding the loss model, we use it to price frost insurance for a general family of insurance contracts that do not generate any risk of moral hazard. In particular, we will find the premiums of stop-loss policies for losses to citrus fruits using Value at Risk, Conditional Value at Risk, and Wang's premium based on temperature data from San Joaquin Drainage in California.     

Undisclosed orders and optimal submission strategies in a limit order market

Reserve orders enable traders to hide a portion of their orders and now appear in most electronic limit order markets. This paper outlines a theory to determine an optimal submission strategy in a limit order book, in which traders choose among limit, market, and reserve orders and simultaneously set price, quantity, and exposure. We show that reserve orders help traders compete for the provision of liquidity and reduce the friction generated by exposure costs. Therefore, total gains from trade increase. Large traders always benefit from reserve orders, whereas small traders benefit only when the tick size is large.     

Is performance measurement and management fit for the future?

Performance measurement and management (PMM) is a management and research paradox. On one hand, it provides management with many critical, useful, and needed functions. Yet, there is evidence that it can adversely affect performance. This paper attempts to resolve this paradox by focusing on the issue of “fit”. That is, in today's dynamic and turbulent environment, changes in either the business environment or the business strategy can lead to the need for new or revised measures and metrics. Yet, if these measures and metrics are either not revised or incorrectly revised, then we can encounter situations where what the firm wants to achieve (as communicated by its strategy) and what the firm measures and rewards are not synchronised with each other (i.e., there is a lack of “fit”). This situation can adversely affect the ability of the firm to compete. The issue of fit is explored using a three phase Delphi approach. Initially intended to resolve this first paradox, the Delphi study identified another paradox – one in which the researchers found that in a dynamic environment, firms do revise their strategies, yet, often the PMM system is not changed. To resolve this second paradox, the paper proposes a new framework – one that shows that under certain conditions, the observed metrics “lag” is not only explainable but also desirable. The findings suggest a need to recast the accepted relationship between strategy and PMM system and the output included the Performance Alignment Matrix that had utility for managers.     

Market or limit orders?

In this paper we investigate the problem of optimal order placement of an asset listed on an exchange using both market and limit orders in a simple model of market dynamics. We seek to understand under which settings it is optimal to place limit or market orders. Limit orders typically lower transaction costs but increase the risk of incomplete order execution, whereas market orders typically have higher transaction costs but are guaranteed to be executed. Rather than considering order book dynamics to determine if a limit order is executed we rely on price dynamics for this. We look at implementation shortfall in this setup with market impact of trading and propose a dynamic program to find the optimal placement of both market and limit orders for risk-neutral and risk-averse traders. With this we find a bound on the expected cost of trading and show that a trader who behaves optimally should always expect to pay less to trade less. We then solve the dynamic program numerically and examine optimal order placement strategies. We find that the decision between market and limit orders is sensitive to price volatility, risk aversion, and trading costs.     

A detailed investigation of the disposition effect and individual trading behavior: a panel survival approach

This article uses a panel survival approach to analyze the trading behavior of foreign exchange traders. We concentrate on a detailed characterization of the shape of the disposition effect over the entire profit and loss regions. In doing so, we investigate the influence of a number of trading characteristics on the impact of the disposition effect. These trading characteristics include: special limit order strategies, trading success, size and the experience of our investors. Our main findings are that (i) the disposition effect has a nonlinear shape. For small profits and losses we find an inverted disposition effect, while for larger ones, the usual positive disposition effect emerges. (ii) The inverted disposition effect is driven to a great extend by patient and cautious investors closing their positions with special limit orders (take-profit and stop-loss). The normal positive disposition effect is found to be intensified for impatient investors closing their positions actively with market orders. (iii) We show that unsuccessful investors reveal a stronger inverse disposition effect. (iv) Evidence that bigger investors are less prone to the disposition effect than smaller investors is also found.     

Price Clustering: Evidence Using Comprehensive Limit-Order Data

Employing comprehensive limit-order data which identify investor types, this paper examines the clustering pattern of limit-order prices. First, limit orders, particularly those submitted by individual investors (IIs), tend to cluster at integer and even prices. Second, nonmarketable limit-order prices cluster more than marketable limit-order prices, indicating that aggressive limit orders generally embed more information. Third, investors choosing even-priced limit orders are not penalized by lower execution ratios. Fourth, investors (particularly IIs) strategically exhibit front-running behavior. Fifth, price clustering indeed creates price barriers. Finally, the degree of price clustering using trade data is significantly underestimated, compared to that using limit-order data.     

Limit Order Trading

We analyze the rationale for limit order trading. Use of limit orders involves two risks: 1) an adverse information event can trigger an undesirable execution, and 2) favorable news can result in a desirable execution not being obtained. On the other hand, a paucity of limit orders can result in accentuated short-term price fluctuations that compensate a limit order trader. Our empirical tests suggest that trading via limit orders dominates trading via market orders for market participants with relatively well balanced portfolios, and that placing a network of buy and sell limit orders as a pure trading strategy is profitable.     

The multivariate Variance Gamma model: basket option pricing and calibration

In this paper, we propose a methodology for pricing basket options in the multivariate Variance Gamma model introduced in Luciano and Schoutens [Quant. Finance 6(5), 385–402]. The stock prices composing the basket are modelled by time-changed geometric Brownian motions with a common Gamma subordinator. Using the additivity property of comonotonic stop-loss premiums together with Gauss-Laguerre polynomials, we express the basket option price as a linear combination of Black & Scholes prices. Furthermore, our new basket option pricing formula enables us to calibrate the multivariate VG model in a fast way. As an illustration, we show that even in the constrained situation where the pairwise correlations between the Brownian motions are assumed to be equal, the multivariate VG model can closely match the observed Dow Jones index options.     

Myopic loss aversion and margin of safety: the risk of value investing

Abstract

Most modern financial markets use a continuous double auction mechanism to store and match orders and facilitate trading. In this paper we develop a microscopic dynamical statistical model for the continuous double auction under the assumption of IID random order flow, and analyse it using simulation, dimensional analysis, and theoretical tools based on mean field approximations. The model makes testable predictions for basic properties of markets, such as price volatility, the depth of stored supply and demand versus price, the bid–ask spread, the price impact function, and the time and probability of filling orders. These predictions are based on properties of order flow and the limit order book, such as share volume of market and limit orders, cancellations, typical order size, and tick size. Because these quantities can all be measured directly there are no free parameters. We show that the order size, which can be cast as a non-dimensional granularity parameter, is in most cases a more significant determinant of market behaviour than tick size. We also provide an explanation for the observed highly concave nature of the price impact function. On a broader level, this work suggests how stochastic models based on zero intelligence agents may be useful to probe the structure of market institutions. Like the model of perfect rationality, a stochastic zero intelligence model can be used to make strong predictions based on a compact set of assumptions, even if these assumptions are not fully believable.     

Resource misallocation,production efficiency and outward foreign direct investment decisions of Chinese enterprises

Resource misallocation, as an essential characteristic of China’s “progressive reform,” has become a significant factor restricting high-quality outward foreign direct investment (OFDI). Using the Database of Chinese Industrial Enterprises (2009–2013), this paper empirically analyzes the impact of resource misallocation on OFDI and its heterogeneous characteristics via the Heckman two-stage selection model. This study finds that total factor productivity has a positive influence on expanding investment quantity. In addition, from the perspective of resource misallocation, factor price distortion inhibits the role of productivity in increasing investment propensity. A mechanism analysis indicates that there is no “productivity paradox” in the decision-making process of OFDI in China as a whole; but there may be an investment productivity paradox in foreign-funded enterprises, enterprises in coastal areas, and large-scale enterprises. After controlling for factor price distortions, the productivity paradox disappears. Thus, total factor productivity (TFP) becomes an important factor in promoting OFDI propensity of overseas-funded enterprises, enterprises in coastal areas and large-scale enterprises.     

Implied Lévy volatility

We study the cause of large fluctuations in prices on the London Stock Exchange. This is done at the microscopic level of individual events, where an event is the placement or cancellation of an order to buy or sell. We show that price fluctuations caused by individual market orders are essentially independent of the volume of orders. Instead, large price fluctuations are driven by liquidity fluctuations, variations in the market's ability to absorb new orders. Even for the most liquid stocks there can be substantial gaps in the order book, corresponding to a block of adjacent price levels containing no quotes. When such a gap exists next to the best price, a new order can remove the best quote, triggering a large midpoint price change. Thus, the distribution of large price changes merely reflects the distribution of gaps in the limit order book. This is a finite size effect, caused by the granularity of order flow: in a market where participants place many small orders uniformly across prices, such large price fluctuations would not happen. We show that this also explains price fluctuations on longer timescales. In addition, we present results suggesting that the risk profile varies from stock to stock, and is not universal: lightly traded stocks tend to have more extreme risks.