All Men Count with You, but None Too Much: Information Aggregation and Learning in Prediction Markets.

Prediction markets are markets that are set up to aggregate information from a population of traders in order to predict the outcome of an event. In this thesis, we consider the problem of designing prediction markets with discernible semantics of aggregation whose syntax is amenable to analysis. For this, we will use tools from computer science (in particular, machine learning), statistics and economics. First, we construct generalized log scoring rules for outcomes drawn from high-dimensional spaces. Next, based on this class of scoring rules, we design the class of exponential family prediction markets. We show that this market mechanism performs an aggregation of private beliefs of traders under various agent models. Finally, we present preliminary results extending this work to understand the dynamics of related markets using probabilistic graphical model techniques. We also consider the problem in reverse: using prediction markets to design machine learning algorithms. In particular, we use the idea of sequential aggregation from prediction markets to design machine learning algorithms that are suited to situations where data arrives sequentially. We focus on the design of algorithms for recommender systems that are robust against cloning attacks and that are guaranteed to perform well even when data is only partially available.PHDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111398/1/skutty_1.pd

Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

Forecasting changes in the South African volatility index. A comparison of methods

Increased financial regulation with tougher capital standards and additional capital buffers has made understanding volatility in financial markets more imperative. This study investigates various forecasting techniques in their ability to forecast the South African Volatility Index (SAVI). In particular, a time-delay neural network’s forecasting ability is compared to more traditional methods. A comparison of the residual errors of all the forecasting tools used suggests that the time-delay neural network and the historical average model have superior forecasting ability over traditional forecasting models. From a practical perspective, this suggests that the historical average model is the best forecasting tool used in this study, as it is less computationally expensive to implement compared to the neural network.  Furthermore, the results suggest that the SAVI is extremely difficult to forecast, with the volatility index being purely a gauge of investor sentiment in the market, rather than being seen as a potential investment opportunity.&nbsp

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. The algorithm is based on divide-and-conquer constraint-based subroutines to learn the local structure around a target variable. We conduct two series of experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning. First, we use eight well-known Bayesian network benchmarks with various data sizes to assess the quality of the learned structure returned by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data. Second, we investigate H2PC's ability to solve the multi-label learning problem. We provide theoretical results to characterize and identify graphically the so-called minimal label powersets that appear as irreducible factors in the joint distribution under the faithfulness condition. The multi-label learning problem is then decomposed into a series of multi-class classification problems, where each multi-class variable encodes a label powerset. H2PC is shown to compare favorably to MMHC in terms of global classification accuracy over ten multi-label data sets covering different application domains. Overall, our experiments support the conclusions that local structural learning with H2PC in the form of local neighborhood induction is a theoretically well-motivated and empirically effective learning framework that is well suited to multi-label learning. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author

From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c

05011 Abstracts Collection -- Computing and Markets

From 03.01.05 to 07.01.05, the Dagstuhl Seminar 05011``Computing and Markets\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Impact of Network Connectedness

학위논문(박사) -- 서울대학교대학원 : 공과대학 산업공학과, 2022. 8. 이재욱.금융 자산은 언제나 리스크에 노출되어 있다. 이 리스크의 크기와, 각 자산이 리스크에 대해 얼마나 보상받는 지를 정확히 측정하는 것은 자산의 특성을 이해하는 데 중요한 문제이다. 자산가격결정모형 (asset pricing model)은 자산의 리스크와 그 보상을 통해서 금융 자산의 수익률을 설명하려 하는 모형이다. 본 연구에서는 여러 자산가격결정모형의 형태 중 팩터 모델에 집중하였다. 팩터 모델은 초과 수익률을 팩터와 베타로 분리해서 설명하는 모델이다. 전통적인 팩터 모델들은 거시 금융 변수나 기업 변수 등을 통하여 팩터와 베타를 추정하는데, 이 때 자산 간의 연결관계를 고려하는 연구는 많이 진행되지 않았다. 금융 자산들은 서로 영향을 주는 관계에 있기 때문에 각각의 수익률 또한 개별적이 아니라 자산 간의 그래프 구조를 고려하며 동시에 평가되어야 한다. 본 논문은 팩터 모델에 자산 간의 연결 구조를 반영하기 위한 인공지능 기반 실증적 자산가격결정모형을 제안한다. 이를 위해 먼저 그래프 인공신경망 (GNN)을 바탕으로 한 멀티 팩터 모델을 개발하였다. 이 때 모델의 구조를 결정하는 것 만큼이나 중요한 것은 자산 간 그래프 구조를 어떻게 정의할 것인가라는 문제이다. GNN은 그 입력 변수로서 잘 정의된 그래프 구조를 요구하지만 자산 간의 연결 구조는 명확하게 정의되지 않았기 때문에, 본 연구에서는 자산 간의 연결성을 피어슨 상관계수를 이용하여 추정하고 이를 특정 임계값을 통해 0과 1로 이진화 시키는 방식을 사용했다. 제안한 모델의 구조는 베타를 추정하는 부분과 팩터를 추정하는 부분으로 나뉘어지는데, 각각 기업 변수와, 수익률을 이용해서 추정한다. 1957년부터 미국에 상장된 주식들을 대상으로 한 실증 실험 결과, 제안한 모델은 설명력과 예측 성능 측면에서 벤치마크 모델들보다 우수한 성능을 보였다. 또한 통계적 성능 이외에도 팩터의 경제적 의미를 측정하는 면에서, 제안한 모델로부터 추정한 팩터가 가장 효율적인 확률적 할인요소 (stochastic discount factor)를 추정할 수 있다는 점 역시 확인하였다. 자산가격결정모형의 가장 중요한 목적은 수익률이지만, 변동성 또한 금융 자산의 움직임을 설명하는 데 중요한 성질이다. 많은 사전 연구에서 밝혀졌듯 수익률과 변동성 사이에는 상관관계가 존재하기 때문에 변동성은 수익률을 설명하는 요인이 될 수 있다. 자산가격결정모형에서와 마찬가지로 자산들 간의 연결 구조를 고려하는 것은 변동성 예측에서도 성능 향상에 큰 영향을 미칠 수 있다. 변동성 분석에서는 여러 자산의 변동성이 서로 영향을 미치는 것을 스필오버 (spillover)라 부른다. 본 논문에서는 스필오버 효과를 직접적으로 반영하는 변동성 예측 모델을 개발하였다. 제안한 모델은 변동성의 측면에서 자산 간 연결 구조를 변동성 스필오버 지수로 구성한 인접행렬로 정의하며, 모델의 구조로는 시공간적 그래프 인공신경망 (spatial-temporal GNN)를 사용하였다. 글로벌 시장 지수들에 대한 실증 실험을 통해서 제안한 모델은 단기와 중기 변동성 예측에서 벤치마크 모델에 비해 가장 좋은 예측 성능을 보이고, 다른 시장에 큰 영향을 주는 시장을 이용하여 다른 시장에 대한 예측 성능을 크게 높일 수 있음을 보였다. 자산가격결정모형에 변동성을 직접적으로 반영하기 위해서는 모형 내에서 변동성이 어떻게 정의되는가를 먼저 살펴보아야 한다. 변동성은 자산가격결정모형 내에서 잔차의 표준편차로 해석할 수 있다. 그러나 시계열 기반 방법론을 사용하여 추정하는 기존의 자산가격결정모형은 시간에 따라 불변하는 변동성을 가정한다. 본 논문에서는 시간에 따라 변하는 변동성을 예측 모델을 이용하여 추정하고, 이를 팩터 모델의 손실함수에 정규화로 사용함으로써 시간에 따라 변화하는 변동성의 특성을 반영하는 팩터 모델을 제안하였다. 미국 상장 주식에 대한 실증 실험 결과 제안한 모델은 시간 불변 변동성 조건을 완화하지 않은 모델에 비해 변동서이 낮은 시기에서 통계적 성능이 큰 폭으로 상승함을 확인하였다. 현재 무시할 수 없는 규모로 성장한 가상화폐 시장에는 구조적으로 확실하게 연결된 자산이 존재한다. 같은 블록체인 상에 존재하는 토큰들은 해당 블록체인 위에서 발행되고 거래되므로 네트워크 구조 상으로 연결성을 지닌다. 본 연구에서는 앞서 진행된 연구에 대한 응용으로, 명확히 구조적으로 연결된 자산들이 초과 수익률을 설명할 수 있는 측정 가능한 공통된 팩터를 가짐을 보이고자 했다. 연구의 대상을 이더리움 블록체인 상의 토큰들로 제한하여 실증 실험을 진행한 결과, EIP-1559 적용 이후에 이더리움 가스 수익률이 시장 수익률과 함께 토큰의 수익률을 설명할 수 있는 팩터로서 작용함을 보였다. 또한, 이더리움 가스 수익률은 토큰의 변동성에 영향을 주는 요소로, 토큰 변동성 예측에도 도움을 줄 수 있는 요소임을 스필오버 기반 변동성 예측 모델을 통해 확인하였다. 본 논문은 자산 간의 연결성을 고려한 자산가격결정모형을 구성하였으며, 이를 통해서 금융 자산들이 갖는 그래프 구조가 실질적으로 수익률에 영향을 미침을 확인할 수 있다. 이 연구결과는 향후 새로운 금융 시장에 대해서도 적용 가능한 확장성 있는 모델이며, 금융 자산의 평가에 있어 여러 자산을 동시에 상관관계를 고려하며 평가해야 한다는 함의점을 제공하고 있다.Financial assets are always exposed to risks. It is important to evaluate the risk properly and figure out how much each asset is compensated for its risk. Asset pricing model explains the behavior of financial asset return by evaluating the risk and risk exposure of asset return. We focused on factor model structure among asset pricing models, which explains excess return through factor and beta coefficients. While conventional factor models estimate factor or beta through various macroeconomic variables or firm-specific variables, there exist fewer studies considering the connectedness between assets. Since financial assets have connected dynamics, asset returns should be priced simultaneously considering the graph structure of assets. In this dissertation, we proposed the AI-based empirical asset pricing model to reflect the connected structure between assets in the factor model. We first proposed the graph neural network-based multi-factor asset pricing model. As important as the structure of the model in constructing an asset pricing model that reflects the structure of the connection between assets is, how to define the connectivity. Graph neural network requires a well-defined graph structure. We defined the connectedness between assets as the binary converted Pearson correlation coefficients of asset returns by the cutoff value. The proposed model consists of a beta estimation part and a factor estimation part, where each part is estimated with firm characteristics and excess returns, respectively. The empirical analysis of U.S equities reveals that the proposed model has more explanatory power and prediction ability than benchmark models. In addition, the most efficient stochastic discount factor can be estimated from the estimated factors. While return is the main object of asset pricing, volatility is also important property for explaining the behavior of financial assets. Volatility can be the factor in explaining return since many studies point out that return and volatility are correlated. As with the asset pricing model, considering the connected structure between assets in volatility prediction can be of great help in explaining the dynamics of assets. In the volatility analysis, what affects between volatility is called spillover. In this aspect, we proposed the volatility prediction model that can directly reflect this spillover effect. We estimated the graph structure between asset volatility using the volatility spillover index and utilized the spatial-temporal graph neural network structure for model construction. From the empirical analysis of global market indices, we confirm that the proposed model shows the best performance in short- and mid-term volatility forecasting. To include volatility in the asset pricing discussion, it is necessary to focus on how volatility is defined in the asset pricing model. In the asset pricing model, volatility can be interpreted as the variance of the residual of the model. However, asset pricing models with time-series estimation mostly have time-unvarying volatility constraints. We constructed an asset pricing model with time-varying volatility by estimating variability using the prediction model and reflecting it in the training loss of the asset pricing model. We identify that the proposed model can improve the statistical performance during the low volatility period through an empirical study of U.S equities. Currently, there are clearly structurally connected assets in the cryptocurrency market, which has grown to a scale that cannot be ignored. All of the same blockchain-based tokens are issued and traded on that blockchain, so they have strong structural connectivity. We tried to identify that an observable factor for explaining excess return exists in such connected tokens as an application of previous studies. We limited the analysis target to Ethereum-based tokens and showed that the Ethereum gas price became a factor for the macroeconomic factor model after the application of EIP-1559. Furthermore, we applied the volatility spillover index-based volatility prediction model using gas return and showed that gas return can increase the prediction performance of certain tokens' volatility.Chapter 1 Introduction 1 1.1 Motivation of the Dissertation 1 1.2 Aims of the Dissertation 10 1.3 Organization of the Dissertation 13 Chapter 2 Graph-based multi-factor asset pricing model 14 2.1 Chapter Overview 14 2.2 Preliminaries 17 2.2.1 Graph Neural Network 17 2.2.2 Graph Convolutional Network 18 2.3 Methodology 19 2.3.1 Multi-factor asset pricing model 19 2.3.2 Proposed method 21 2.3.3 Forward stagewise additive factor modeling 23 2.4 Empirical Studies 24 2.4.1 Data 24 2.4.2 Benchmark models 24 2.4.3 Empirical results 28 2.5 Chapter Summary 33 Chapter 3 Volatility prediction with volatility spillover index 37 3.1 Chapter Overview 37 3.2 Preliminaries 41 3.2.1 Realized Volatility 41 3.2.2 Volatility Spillover Measurements 42 3.2.3 Benchmark Models 45 3.3 Empirical Studies 50 3.3.1 Data 50 3.3.2 Descriptive Statistics 51 3.3.3 Proposed Method 52 3.3.4 Empirical Results 54 3.4 Chapter Summary 61 Chapter 4 Graph-based multi-factor model with time-varying volatility 64 4.1 Chapter overview 64 4.2 Preliminaries 67 4.2.1 Local-linear regression for time-varying parameter estimation 67 4.3 Methodology 68 4.3.1 Time-varying volatility implied loss function 68 4.3.2 Proposed model architecture 70 4.4 Empirical Studies 72 4.4.1 Data 72 4.4.2 Benchmark Models 72 4.4.3 Empirical Results 73 4.5 Chapter Summary 79 Chapter 5 Macroeconomic factor model and spillover-based volatility prediction for ERC-20 tokens 82 5.1 Chapter Overview 82 5.2 Preliminaries 85 5.3 Methodology 86 5.3.1 Relation analysis 86 5.3.2 Factor model analysis 89 5.3.3 Volatility prediction with volatility spillover index 90 5.4 Empirical Studies 90 5.4.1 Data 90 5.4.2 Empirical Results 98 5.5 Chapter Summary 102 Chapter 6 Conclusion 105 6.1 Contributions 105 6.2 Future Work 108 Bibliography 109 국문초록 130박

Essays on Natural Language Processing and Central Banking.

Humans generally interact, communicate, and form social structures using natural language. Due to the high dimensionality of language, much of the wealth of information from these interactions has been barred from the economic profession. However, recent technological advancements lead to increasing use of text as an underlying datasource in economic and financial applications. This trend has been further accelerated by Nobel laureate Robert J. Shiller's presidential address to the American Economic Association "Narrative Economics", in which he argues for more elaboration on narratives - stories that affect individual decisions and collective actions - by the economic scientific community. Addressing this gap in the literature, research has been published utilizing textual information to quantify latent variables such as uncertainty, forecasting macroeconomic variables in real time, and asset price predictions. In conjunction with the rise of natural language processing applications, there has been a shift in perspective on monetary policy with regards to central bank transparency and communication. Transitioning from the presumption that monetary policy is limited to interest rate actions, communication has advanced to become a key tool in the central banker's toolbox. Ever since, words are used to anchor expectations and self-enforce the central banks' desired equilibrium path. As a result, research on monetary policy has been relentless in the pursuit of adopting novel techniques as well as incorporating new unstructured data sources such as news-articles, press conference statements, and speeches. This string of literature is regularly complemented by an extension of the traditional empirical toolbox, borrowing novel techniques from the field of machine learning. The here presented cumulative dissertation consists of four essays that touch on all these fields, namely text as data, monetary policy, and machine learning. My primary focus is on the European Central Bank (ECB), but the methodology and ideas can be extended to other central banks as well. Throughout this thesis, textual information is incorporated from different data sources, analyzed using different techniques in order to approximate different latent variables. As a result, text is employed as a dependent variable at times and as an independent variable at other times. Specifically, the first essay leverages the relative frequency of terms used in ECB press statements as anecdotal evidence for the diversity of the central banks' communication with regard to their topics, whereas the second essay counts positive and negative terms in speeches to approximate the latent variable of central bank loss. The third essay examines the impact of linguistic complexity on financial market participants by conducting a readability test on the ECB's introduction statements, and the final essay dives into computational linguistics to develop a novel central bank-specific language model for better quantifying monetary policy communication. The following is a brief summary of the four essays included in this thesis. My first essay analyzes rule-based monetary policy in the euro area before and after the financial crisis. Jonas Gross and I argue that the environment in which policymakers operate is far more complex than traditional model-based analysis of policy rules permits. We complement this view with evidence from ECB press conferences, demonstrating that the central bank discusses a wide range of topics beyond the traditional Taylor-rule variables. Since each variable has the potential to be relevant in understanding the central bank's reaction function, we combine a literature review with natural language processing to identify a set of potential determinants. The traditional approach of selecting a single interest rate response function is then contrasted by applying a Bayesian model averaging approach to these determinants. We account for model uncertainty by including a large number of determinants and estimating a total of 33.000 different model combinations. Our results suggest that in contrast to the ongoing criticism, the ECB primarily reacts to inflation in its interest rate decision. In fact, our analysis finds that inflation is a significant variable in almost all of the examined model combinations. Furthermore, we find that the ECB reacts to changes in economic activity determinants such as unemployment and production as well. These economic activity indicators were a priority for the ECB prior to the financial crisis but have since declined in relevance, suggesting that inflation is the sole driver of monetary policy decisions in the post-crisis period. Finally, we assess our findings with textual evidence from the ECB press conferences, where, in accordance with the previous results, we find the same shift. My second essay focuses on the ECB's objective itself, quantifying the central bank's satisfaction with current economic conditions through textual analysis. By maximizing an implied objective function, the ECB is assumed to pursue inflation targeting with a subordinate focus on supporting the general economic policy of the European Union. I compute the central bank's sentiment using the ECB's public communication by counting the number of positive and negative words in speeches, allowing me to quantify the objective. Assuming a typical functional form for the objective allows me to estimate the optimal levels with respect to inflation and economic activity, i.e. the bliss points in which the central banks communication is the most positive. Using a dictionary approach to estimate the sentiment index yields several interesting results. The most surprising is, unquestionable, a concave inflation objective with an implied inflation target beyond the banks' mandate and best described as 'above, but close to 2%'. Deviations from this bliss point appear to lower the satisfaction, and hence the optimistic language in speeches. With respect to the subordinate objective, I find a convex objective towards output growth and a linear objective towards the unemployment rate. Furthermore, my results suggest that deviations from the primary objective, the inflation rate, appear to have no greater effect on the speeches' language than deviations from either of the subordinate objectives. In fact, in contrast to inflation, both output and unemployment are consistently significant variables. Finally, contrary to findings in the United States, financial market conditions have no significant influence on the ECB's sentiment. In the third essay, Bernd Hayo, Kai Henseler, Marc Steffen Rapp, and I investigate the impact of central bank communication on financial markets. We are particularly interested in the communication's complexity and how it affects financial market trading. To examine this relationship empirically, we employ high-frequency data from European stock index futures during the introductory statement of the ECB's press conferences. A readability test on the introductory statement during the press conference determines the statements' linguistic complexity. In conjunction with the central banks' unique communication design, we are able to separate the effect of verbal complexity on trading during the introductory statements and the subsequent Q&A session. Our sample contains announcements of novel UMPM, enabling us to investigate whether the content of the introductory statements interacts with the reaction of traders to its linguistic complexity. We find that the Q&A sessions are - in terms of linguistic complexity - less complex and thus more comprehensible. When UMPM are announced, contemporaneous trading volumes are negatively correlated with complexity, resulting in a temporal shift of trading towards the less complex Q&A session. This shift is first indication that financial markets respond to linguistic complexity in a context-specific manner. This line of reasoning is strengthened further by the observation that events containing UMPM are less similar in terms of wording to previous statements. As a result, we believe that financial market traders are underreacting to novel complex information in introductory statements regarding UMPM. The subsequent discussion and clarification of the cognitively costly content during the Q&A session mitigates this effect, shifting trading from the introductory statement phase to the Q&A phase of the ECB's press conference. The final essay concerns the quantification of central bank communication, i.e. it explores how text in monetary policy can be effectively summarised and analysed. Martin Baumgärtner and I propose a novel language model, build on machine learning, as a tool to quantify central bankers qualitative information. The necessity and feasibility of measuring central bank communication in this manner stems from two major developments in the fields of monetary policy and machine learning over the last two decades. On the one hand, central bankers' communication, as well as its analysis, has increased substantially. This progress necessitates some form of quantification of the qualitative components, a research topic dominated by dictionary approaches. On the other hand, advances at the intersection of linguistics and computer science enabled the use of machine learning to train language models capable of adequately capturing the languages multidimensionality and context-dependence. The resulting models are regularly open source. However, the technical jargon of central bankers renders them generally unsuitable for use in the field. This essay aims to apply computational linguistics research to monetary policy by developing a language model exclusively trained on central bank communication. To accomplish this, we gather a large and diverse text corpus, which we use to compare a number of state-of-the-art machine learning algorithms. Choosing the most promising, we develop a central bank specific language model. Several applications are presented to showcase the broad applicability of our language model. First, we propose a novel technique for comparing central banks, affirming that similarity is driven by mutual objectives. Next, we construct a time-series index that reflects the ECB's willingness to act as a lender of last resort. The index suggests that communication similar to Mario Draghi's 'whatever it takes' speech can calm financial markets during times of high uncertainty. The third application emphasizes the presence of prejudices even in central bankers' technical language. We demonstrate how social patterns, such as occupational gender distribution, are reflected in their communication. The final application is a forecasting exercise that suggests that speeches may be more accurate predictors than previous research suggests