Sandra Nolte (Lechner)

How Do Individual Investors Trade

This paper examines how high-frequency trading decisions of individual investors are influenced by past price changes. Specifically, we address the question as to whether decisions to open or close a position are different when investors already hold a position compared with when they do not. Based on a unique data set from an electronic foreign exchange trading platform, OANDA FXTrade, we find that investors’ future order flow is (significantly) driven by past price movements and that these predictive patterns last up to several hours. This observation clearly shows that for high-frequency trading, investors rely on previous price movements in making future investment decisions. We provide clear evidence that market and limit orders flows are much more predictable if those orders are submitted to close an existing position than if they are used to open one. We interpret this finding as evidence for the existence of a monitoring effect, which has implications for theoretical market microstructure models and behavioral finance phenomena, such as the endowment effect.

The Multiplicative Simulation-Extrapolation Approach

We develop a new general approach for handling multiplicative measurement error in continuous covariates in linear and nonlinear regression models. We apply the Simulation-Extrapolation (SIMEX) approach, which is a simulation based method of estimating and reducing the bias due to additive measurement error, to the case of multiplicative measurement error. We do not apply a logarithmic transformation, so that the multiplicative measurement error model becomes an additive one, but we show how to modify the SIMEX approach, in order to use the multiplicative measurement error model as such. Multiplying the measurement error by additional measurement error allows us to infer in which way the estimation bias is affected by the increase of variance of the measurement error. In the extrapolation step, the estimated parameters are modelled as a function of the magnitude of the variance of the measurement error and extrapolated to the case of no measurement error. We apply our method to the case of data masking, in order to obtain parameter estimates of the true data generating process, if the data are multiplied by an additional measurement error. We produce Monte-Carlo evidence on how the reduction of data quality can be minimized by masking.

Customer Trading in the Foreign Exchange Market Empirical Evidence from an Internet Trading Platform

This paper analyzes the relationship between currency price changes and their expectations. Currency price change expectations are derived with the help of different order flow measures, from the trading behavior of investors on OANDA FXTrade, which is an internet trading platform in the foreign exchange market. We investigate whether forecasts of intra-day price changes on different sampling frequencies can be improved with the information contained in the flow of our investors’ orders. Moreover, we verify several hypotheses on the trading behavior and the preference structure of our investors by investigating how past price changes affect future order flow.

Sell-Side Analysts' Career Concerns during Banking Stresses

We propose a new approach to examine sell-side analysts’ career concerns by relating their forecast boldness to their employers’ news flows. Specifically, we use banking sector news to proxy for the severity of career concerns. Analysts follow more closely the consensus forecast when the prospects of the banking sector are negative (and vice versa). The effect is both economically and statistically significant after controlling for various firm, analyst, brokerage house, and forecasting characteristics, as well as sector and economy wide effects. The more established analysts, in terms of reputation and experience, are generally unaffected by banking sector news. In contrast, their less established peers tend to cluster their forecasts near the consensus after a sequence of negative news flows for banks. Collectively, our results support the notion that during banking stresses when job security is low analysts’ tendency to imitate others increases.

Where Do the Joneses Go on Vacation? Social Distance and the Influence of Online Reviews on Product Sales

This paper provides field evidence that social distance between customers and reviewers influences the impact from online reviews on product sales. We conceptualize information on interpersonal similarity as a heuristic cue that helps customers to infer similarity in product preferences between reviewers and customers. We hypothesize that customers put less weight on information from socially distant others. We test this hypothesis on an extensive dataset of 60,000 hotel-week observations from a large online travel and holiday portal. The data allows for measuring interpersonal similarity in a clean way, because reviewers need to specify whether they traveled as couples, families, or singles, and reviews can be traced back to corresponding bookings. Therefore, we know for each reviewer, if at the time of booking her peers were couples, families, or singles. Our results provide strong empirical support for our hypothesis. For example, singles increase hotel demand by 18% in response to positive peer information, but discard family information. We conclude that firms’ management of online reviews must reflect on social distance between reviewers and target customers.

Dimensions and location of high-involvement management: fresh evidence from the UK Commission's 2011 Employer Skills Survey: High-involvement management

High-involvement management is typically seen as having three components: worker involvement, skill and knowledge acquisition and motivational supports. The prescriptive literature implies the elements should be used together; but using data from the UK Commissions Employer Skills Survey of 2011 we find that these dimensions of high-involvement management are in reality separate. Two types of involvement, role and organisational, are not strongly related, and motivational supports are not strongly correlated with other practices or each other. Size of workplace and the sector in which it operates are associated with the dimensions of high-involvement management. However, there is variety in their other predictors. For example, organisational involvement and skill acquisition are positively related to workplace size while role involvement is negatively associated with it. The research illustrates the value of scaling methods over blanket indexes to measure high involvement management and highlights the independent effects of quality and operational management methods.

Multiplicative Measurement Error and the Simulation Extrapolation Method

Whereas the literature on additive measurement error has known a considerable treatment, less work has been done for multiplicative noise. In this paper we concentrate on multiplicative measurement error in the covariates, which contrary to additive error not only modi es proportionally the original value, but also conserves the structural zeros. This paper compares three variants to specify the multiplicative measurement error model in the simulation step of the Simulation-Extrapolation (SIMEX) method originally proposed by Cook and Stefanski (1994): i) as an additive one without using a logarithmic transformation, ii) as the well-known logarithmic transformation of the multiplicative error model, and iii) as an approach using the multiplicative measurement error model as such. The aim of the paper is to analyze how well these three approaches reduce the bias caused by the multiplicative measurement error. We apply three variants to the case of data masking by multiplicative measurement error, in order to obtain parameter estimates of the true data generating process. We produce Monte Carlo evidence on how the reduction of data quality can be minimized.

Asymptotic Theory for Renewal Based High-Frequency Volatility Estimation

This paper develops the idea of renewal time sampling, a novel sampling scheme constructed from stopping times of semimartingales. Based on this new sampling scheme we propose a class of volatility estimators named renewal based volatility estimators. In this paper we show that: (1) The spot variance of a continuous martingale can be expressed in terms of the conditional intensity or conditional duration density of renewal sampling times; (2) In an infill asymptotics setting, renewal based volatility estimators are consistent and jump-robust estimators of the integrated variance of a general semimartingale; (3) Renewal time sampling and range-based sampling have a higher sampling efficiency than equidistant return-based sampling.

High-Frequency Volatility Estimation and the Relative Importance of Market Microstructure Variables: An Autoregressive Conditional Intensity Approach

In this paper we use an autoregressive conditional intensity approach to estimate local high-frequency volatility, and examine to what extent a large universe of market microstructure variables affects local volatility. Our findings support a sequential information arrival hypothesis on the high-frequency level since we show that contemporaneous trading volume is negatively, and lagged trading volume is positively related to local volatility. The use of a penalized likelihood method allows us to obtain a ranking in terms of the relative importance of all market microstructure variables considered. We find that, in a descending order, contemporaneous volume, bid-ask spread, absolute order imbalance, absolute order flow and absolute quote difference carry the most important information for local volatility modelling.

The Information Content of Retail Investors' Order Flow

In this paper, we provide evidence that the trading activity of small retail investors carries significant genuine information that can be exploited for the short-term out-of-sample forecasting of foreign exchange rates. Our findings are based on a unique dataset of around 2000 retail investors from the OANDA FXTrade electronic trading platform. Our results are consistent with the view that in the foreign exchange market private information is highly dispersed, but can be extracted by observing customer order flow. Previous studies, however, focused on the information content of costumer order flow of dealers in the interbank market, whose clients are themselves large institutional and professional investors. Our study is the first that analyzes a crowd of small retail investors and shows that even the trading activity of these investors contains, on aggregate, important non-public information that can be exploited for short-term exchange rate forecasting. Our findings lead us to conjecture that retail investors (on aggregate) are not pure noise traders but process dispersed information at least partially in a similar way as large institutional investors and hence place their orders accordingly.