MARKET MAKING WITH ALPHA SIGNALS

We show how a market maker employs information about the momentum in the price of the asset (i.e. alpha signal) to make decisions in their liquidity provision strategy in an order-driven electronic market. The momentum in the midprice of the asset depends on the execution of liquidity taking orders and the arrival of news. Buy market orders (MOs) exert a short-lived upward pressure on the midprice, whereas sell MOs exert a short-lived downward pressure on the midprice. We employ Nasdaq high-frequency data to estimate model parameters and to illustrate the performance of the market making strategy. The market maker employs the alpha signal to minimise adverse selection costs, execute directional trades in anticipation of price changes, and to manage inventory risk. As the market maker increases their tolerance to inventory risk, the expected profits that stem from the alpha signal increase because the strategy employs more speculative MOs and performs more roundtrip trades with limit orders.

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Os efeitos da introdução de agentes de liquidez no mercado acionário brasileiro

Brazilian Review of Finance ◽

10.12660/rbfin.v11n2.2013.4088 ◽

2013 ◽

Vol 11 (2) ◽

pp. 281

Author(s):

Marcelo Perlin

Keyword(s):

Event Study ◽

High Frequency ◽

Financial Institution ◽

Stock Exchange ◽

Market Maker ◽

Average Increase ◽

Market Making ◽

Market Makers ◽

Liquidity Provision ◽

Before And After

The main objective of this study is to analyze the empirical effects of the introduction of market makers in the Brazilian stock exchange. By aggregating information regarding the dates of the market maker’s contract and the use of a privileged high frequency database, it was possible to execute an event study to check the effect of the introduction of liquidity agents. As expected, the period after the beginning of the market maker’s contract presented a significant increase in the liquidity of the stocks. The study reports an average increase of 31% in the number of trades in the period before and after the start of the contract. Another result is that the work of a liquidity agent can change significantly the autocorrelation of the trade signs in approximately 10%. Such a result is stronger for the stocks with lower liquidity. The investigation also shows heterogeneous results for the performance of the liquidity provision when the analysis based itself on the financial institution of the market maker. Such information is particularly important for companies that are seeking to contract market making services.

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MARKET MAKING AND PORTFOLIO LIQUIDATION UNDER UNCERTAINTY

International Journal of Theoretical and Applied Finance ◽

10.1142/s0219024914500344 ◽

2014 ◽

Vol 17 (05) ◽

pp. 1450034 ◽

Cited By ~ 5

Author(s):

KAJ NYSTRÖM ◽

SIDI MOHAMED OULD ALY ◽

CHANGYONG ZHANG

Keyword(s):

Reference Price ◽

Probabilistic Models ◽

Practical Importance ◽

Model Parameters ◽

Market Maker ◽

Model Risk ◽

Market Making ◽

Optimal Liquidation ◽

Limit Orders ◽

Order Books

Market making and optimal portfolio liquidation in the context of electronic limit order books are of considerably practical importance for high frequency (HF) market makers as well as more traditional brokerage firms supplying optimal execution services for clients. In general, the two problems are based on probabilistic models defined on certain reference probability spaces. However, due to uncertainty in model parameters or in periods of extreme market turmoil, ambiguity concerning the correct underlying probability measure may appear and an assessment of model risk, as well as the uncertainty on the choice of the model itself, becomes important, as for a market maker or a trader attempting to liquidate large positions, the uncertainty may result in unexpected consequences due to severe mispricing. This paper focuses on the market making and the optimal liquidation problems using limit orders, accounting for model risk or uncertainty. Both are formulated as stochastic optimal control problems, with the controls being the spreads, relative to a reference price, at which orders are placed. The models consider uncertainty in both the drift and volatility of the underlying reference price, for the study of the effect of the uncertainty on the behavior of the market maker, accounting also for inventory restriction, as well as on the optimal liquidation using limit orders.

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Can high-frequency data enable better parameterization of water quality models and disentangling of DO processes?

10.5194/egusphere-egu21-8936 ◽

2021 ◽

Author(s):

Jingshui Huang ◽

Pablo Merchan-Rivera ◽

Gabriele Chiogna ◽

Markus Disse ◽

Michael Rode

Keyword(s):

Water Quality ◽

High Frequency ◽

Critical Role ◽

Measurement Data ◽

High Frequency Data ◽

Model Parameters ◽

Frequency Data ◽

Quality Models ◽

Model Parameter ◽

Water Quality Models

Water quality models offer to study dissolved oxygen (DO) dynamics and resulting DO balances. However, the infrequent temporal resolution of measurement data commonly limits the reliability of disentangling and quantifying instream DO process fluxes using models. These limitations of the temporal data resolution can result in the equifinality of model parameter sets. In this study, we aim to quantify the effect of the combination of emerging high-frequency monitoring techniques and water quality modelling for 1) improving the estimation of the model parameters and 2) reducing the forward uncertainty of the continuous quantification of instream DO balance pathways.To this end, synthetic measurements for calibration with a given series of frequencies are used to estimate the model parameters of a conceptual water quality model of an agricultural river in Germany. The frequencies vary from the 15-min interval, daily, weekly, to monthly. A Bayesian inference approach using the DREAM algorithm is adopted to perform the uncertainty analysis of DO simulation. Furthermore, the propagated uncertainties in daily fluxes of different DO processes, including reaeration, phytoplankton metabolism, benthic algae metabolism, nitrification, and organic matter deoxygenation, are quantified.We hypothesize that the uncertainty will be larger when the measurement frequency of calibrated data was limited. We also expect that the high-frequency measurements significantly reduce the uncertainty of flux estimations of different DO balance components. This study highlights the critical role of high-frequency data supporting model parameter estimation and its significant value in disentangling DO processes.

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Welfare Effects of Market Making in Continuous Double Auctions

Journal of Artificial Intelligence Research ◽

10.1613/jair.5360 ◽

2017 ◽

Vol 59 ◽

pp. 613-650 ◽

Cited By ~ 1

Author(s):

Elaine Wah ◽

Mason Wright ◽

Michael P. Wellman

Keyword(s):

Allocative Efficiency ◽

Market Model ◽

Model Parameters ◽

Market Maker ◽

Market Making ◽

Market Makers ◽

Continuous Double Auctions ◽

Thin Markets ◽

Heuristic Strategies ◽

Fundamental Value

We investigate the effects of market making on market performance, focusing on allocative efficiency as well as gains from trade accrued by background traders. We employ empirical simulation-based methods to evaluate heuristic strategies for market makers as well as background investors in a variety of complex trading environments. Our market model incorporates private and common valuation elements, with dynamic fundamental value and asymmetric information. In this context, we compare the surplus achieved by background traders in strategic equilibrium, with and without a market maker. Our findings indicate that the presence of the market maker strongly tends to increase total welfare across various environments. Market-maker profit may or may not exceed the welfare gain, thus the effect on background-investor surplus is ambiguous. We find that market making tends to benefit investors in relatively thin markets, and situations where background traders are impatient, due to limited trading opportunities. The presence of additional market makers increases these benefits, as competition drives the market makers to provide liquidity at lower price spreads. A thorough sensitivity analysis indicates that these results are robust to reasonable changes in model parameters.

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