Aggregate Market Research Articles

Spatial clustering: Influence of urban street networks on retail sales volumes

As is often believed that the more centrally located a shop, the higher its sales volume, this paper analyzed relationships between the spatial clustering of retail stores, their respective transaction volumes, and the urban street networks to determine whether, and to what extent, the accessibility and density of a store’s location was correlated with its transaction volume. While this hypothesis is widely accepted, its veracity is underexplored and rarely validated using large-scale empirical datasets, possibly owing to the lack of access. Therefore, transaction datasets and accessibility indicators were first examined; a clear, positive correlation between density and revenue was found for specialty stores wherein people do “comparison shopping,” and for stores that complemented each other for activities such as “one-trip shopping,” the revenues were positively correlated when the stores were clustered. Generally, daily-use stores’ revenues were more sensitive to local access and those of non-daily-use stores were more sensitive to global access. In conclusion, these findings would not have been found using conventional methodology focused on the retail sector as a whole, because aggregate market mechanisms would have hidden the observed effects on specific store categories. Therefore, upon disaggregating the data, we found a distinct heterogeneity across the different store types for what concerns the relationship between revenue and location.

Retail Investors’ Trading and Stock Market Liquidity

The paper investigates the relation between retail investors’ participation in trading and aggregate stock market liquidity. The findings show a positive and significant relation between retail investors’ trading and stock market liquidity. Examination of the determinants of retail investors’ trading reveals that, on average, retail investors with more diversified trading activity tend to trade when liquidity is higher, the frequency of their arrival to the market is not affected by the level of liquidity, and retail investors are willing to trade at a lower liquidity level as sellers than as buyers. Moreover, retail investors’ trading does not create price noise at the aggregate market level. Overall, the evidence suggests that retail investors contribute to market quality.

Retail investors’ trading and stock market liquidity

The paper investigates the relation between retail investors’ participation in trading and aggregate stock market liquidity. The findings show a positive and significant relation between retail investors’ trading and stock market liquidity. Examination of the determinants of retail investors’ trading reveals that, on average, retail investors with more diversified trading activity tend to trade when liquidity is higher, the frequency of their arrival to the market is not affected by the level of liquidity, and retail investors are willing to trade at a lower liquidity level as sellers than as buyers. Moreover, retail investors’ trading does not create price noise at the aggregate market level. Overall, the evidence suggests that retail investors contribute to market quality.

Measuring liquidity in Indian stock market: A dimensional perspective.

Market liquidity ensures the marketability of security and is an indispensable feature of stock markets. Previous studies have emphasized the role of stock market liquidity in empirical finance. However, they have inadequately explored its multidimensional nature. This study eliminates the ambiguities related to market liquidity by precisely measuring it by using popular and proven liquidity measures. As such, the present study aims to evaluate market liquidity in terms of depth, breadth, tightness, and immediacy in the Indian equity market and also identifies crucial interdependencies between liquidity dimensions. The study selects 500 stocks constituting the NIFTY 500 index of the National Stock Exchange, India, as of 26th May 2019. The data on trading volume, bid price, ask price, the number of shares outstanding, closing share prices were retrieved for the period from 1st April 2009 to 31st March 2019. The study employs Share Turnover, Amihud Illiquidity Ratio, Relative Quoted Spreads, and Coefficient of Elasticity of Trading for liquidity measurement. The Vector Auto-Regressive (VAR) model is used to establish the simultaneous relationships between liquidity dimensions. The analysis is conducted at the aggregate market level as well as across turnover based stock groups divided based on their rankings in terms of stock specific share turnover. The empirical results evidenced the presence of consistent depth, strong breadth, and immediacy but lower tightness in the Indian equity market. The market depth and tightness appear to be relevant in determining dimensional interdependencies. Also, less frequently traded stocks exhibit higher illiquidity in the wake of lower tightness. The findings of this study will assist the investors to wisely understand the multifaceted nature of market liquidity and base their trading decisions accordingly. Moreover, the regulators of the stock exchange can devise liquidity enhancing policies based on the directional movements among liquidity dimensions.

Oil stocks, risk factors, and tail behavior

Abstract Early studies of oil stocks focus exclusively on the average relationship between oil price changes and individual stock returns. In this paper, we examine how the tail behavior of risk factors affects the tail behavior of individual oil stock returns. We consider a total of 25 widely-used risk factors from the asset pricing literature. These risk factors include 14 stock market factors, three bond market factors, and eight commodity market factors. We find that the excess stock market return, the change in CBOE aggregate market volatility index, the commodity market index return, the change in the prices of oil futures contracts, and the change in CBOE oil ETF volatility index have the largest impact in moving oil stocks tail returns.

Economic Valuation of Cultural Ecosystem Services: The Case of Landscape Aesthetics in the Agritourism Market

Natural and agricultural landscapes provide a wide range of ecosystem services, among them aesthetic landscapes. Since these services have no market value, land use decision makers often ignore them in favor of urban sprawl, with the result that resource allocation may be sub-optimal. Here we suggest a novel method to evaluate the aesthetic landscape services of natural and agricultural ecosystems using the case of the agritourism market in Israel. We model the agritourism market as an oligopolistic market with differentiated products and formulate an equilibrium model with structural, double nested logit demand and pricing equations. The structural equations are expressed as a function of the attributes of the agritourism firm, among which are the components of landscape view. We use aggregate market data and GIS data to estimate the structural model. In the case of urban sprawl, the welfare loss is estimated at between US$108,000 thousand and US$197,000 per km2, depending on the type of ecosystem that is forgone, while in the case of agricultural sprawl over natural areas, the welfare loss is estimated at US$141,000 per km2. This welfare loss can be considered the economic value of landscape aesthetics services to the agritourism market. These findings illustrate the potential of using this valuation method for other ecosystems in other markets.

Industry equi-correlation: A powerful predictor of stock returns

We show that the detrended equi-correlation of the returns of industry portfolios is a strong predictor of excess returns to the S&P 500 Index. Using a sample from 1927 to 2015, our monthly industry equi-correlation (IEC) index produces an out-of-sample R 2 of as high as 0.888%. For an investor with mean–variance utility, the IEC index can generate utility gains of 120.5 basis points over the benchmark model of the historical average. The return predictability of the IEC index is stronger than that of all of the popular predictor variables. Furthermore, we find that incorporating IEC in a univariate predictive regression with a popular predictor can significantly improve the out-of-sample forecasting performance of the individual models and their forecast combinations. These findings are confirmed by a large battery of robustness checks. • The equi-correlation of industry return (IEC) can predict aggregate market return. • The monthly IEC index produces an out-of-sample R 2 of 0.888%. • The IEC index generates utility gains of 120.5 bps over the benchmark model. • Adding an IEC index to a popular predictor model improves predictive ability.

Unconventional Times: Demand Shocks, Competition, Capability, and Innovation

We develop a formal model to predict innovation as a choice affected by industry competition, market size, and firm heterogeneity in technological capability. Our model first predicts a selection effect, that the threshold of firm capability for entry will decrease with aggregate market size (holding competition constant) and increase with competition (holding market size constant). In addition, it predicts two opposing effects of market size on innovation: A direct positive effect due to increased demand, and an indirect negative effect cue to increased competition induced by the lessened selection effect. Furthermore, our model predicts heterogeneous responses to macro demand shocks and competition across firms with different capability. We test these predictions by comparing innovations adopted by operators of unconventional wells in the United States before and after the 2014 oil price crash.

Illiquidity and Stock Returns II: Cross-section and Time-series Effects

Abstract Lou and Shu decompose Amihud’s illiquidity measure (ILLIQ) proposing that its component, the average of inverse dollar trading volume (IDVOL), is sufficient to explain the pricing of illiquidity. Their decomposition misses a component of ILLIQ that is related to illiquidity. We find that this component affects stock returns significantly, both in the cross-section and in time-series. We show that the ILLIQ premium is significantly positive after controlling for mispricing, sentiment, and seasonality. In addition, the aggregate market ILLIQ outperforms market IDVOL in estimating the effect of market illiquidity shocks on realized stock returns.

Equity return predictability, its determinants, and profitable trading strategies

AbstractThis paper explains cross‐market variations in the degree of return predictability using the extreme bounds analysis (EBA). The EBA addresses model uncertainty in identifying robust determinant(s) of cross‐sectional return predictability. Additionally, the paper develops two profitable trading strategies based on return predictability evidence. The result reveals that among the 13 determinants of the cross‐sectional variation of return predictability, only value of stock traded (a measure of liquidity) is found to have robust explanatory power by Leamer's (1985) EBA. However, Sala‐i‐Martin's (1997) EBA reports that value of stock traded, gross domestic product (GDP) per capita, level of information and communication technology (ICT) development, governance quality, and corruption perception are robust determinants. We further find that a strategy of buying (selling) aggregate market portfolios of the countries with the highest positive (negative) return predictability statistic in the past 24 months generates statistically significant positive returns in the subsequent 3 to 12 months. In the individual country level, a trading rule of buying (selling) the respective country's aggregate market portfolio, when the return predictability statistic turns out positive (negative), outperforms the conventional buy‐and‐hold strategy for many countries.

Causality between Environment and Financial Development in case of Pakistan: A Time Series Analysis

Climate change is now widely regarded as one of the greatest issues of our era. As a result, it is critical to study the impact of various macroeconomic variables on the environment. The purpose of this research is to look at the link between financial development and environmental (CO2 emissions) in Pakistan from 1980 to 2014. The Auto Regressive Distributed Log (ARDL) technique was employed to track both long-term and short-term association between variables in this investigation. Granger causal testing is used to examine the causal relationship. Study results suggest that there is a unidirectional causality between CO2 and the financial development Index. However, the environment (CO2 emissions) in the long run will depend on the financial development that the financial development index (domestic credit to private sector aggregate market capitalization, and FDI) has in Pakistan. In the second model, the environment (energy consumption) also depends on financial development.

Derivation of a Dynamic Market Risk Signal Using Kernel PCA and Machine Learning

Kernel principal component analysis (PCA) is an extension of the conventional PCA method that employs a kernel transformation whereby hidden patterns in possibly multidimensional data may be detected and extracted more explicitly. In this article, the author applies the method of kernel PCA to a currency prediction case study and derives an aggregate market signal. It is observed that this signal has desirable information-compression properties and may be used as a predictive risk indicator in the return prediction models. Used alongside common drivers of exchange rates, a kernel PCA signal enhances in-sample and out-of-sample risk-adjusted performance across a range of machine learning strategies. In particular, the author observes that a kernel PCA signal remains robust and predictive during volatile market conditions. The kernel PCA signal may be used as a machine learning feature to inform and support data-driven risk management strategies. TOPICS:Big data/machine learning, derivatives Key Findings • This article develops a risk signal based on the method of kernel PCA. • Kernel PCA conveys desirable information on market dynamics. • Currency machine learning strategies benefit from this kernel PCA feature.

Institutional Norms and the Institutionalization of Macromarketing: Historical Insights, the Long Macro View, and Service-Dominant Logic

Progress toward the institutionalization of macromarketing continues to occur. This article addresses the suggestion, implicit in Robert F. Lusch’s “Long Macro View,” that the institutional norms of macromarketing deserve scrutiny. Specifically, this article argues that insights on macromarketing’s existing and appropriate institutional norms can be found in (1) the first, truly marketing, academic journal article ever written, “Some Problems in Market Distribution,” (2) the first macromarketing conference held at the University of Colorado in 1976, (3) the “four eras” historical analysis of aggregate marketing systems by William Wilkie and Elizabeth Moore, and (4) the service- dominant-logic of Stephen Vargo and Robert F. Lusch. This article argues that these works suggest fifteen institutional norms for macromarketing scholars to consider, discuss, and evaluate.

Forecasting aggregate market volatility: The role of good and bad uncertainties

AbstractWe decompose economic uncertainty into "good" and "bad" components according to the sign of innovations. Our results indicate that bad uncertainty provides stronger predictive content regarding future market volatility than good uncertainty. The asymmetric models with good and bad uncertainties forecast market volatility in a better way than the symmetric models with overall uncertainty. The combination for asymmetric uncertainty models significantly outperforms the benchmark of autoregression, as well as the combination for symmetric models. The revealed volatility predictability is further demonstrated to be economically significant in the framework of portfolio allocation.

Competition is (still) a tough weed: A review essay of Thomas Philippon’s The great reversal: How America gave up on free markets

In The Great Reversal, Philippon makes the case that competition in America has weakened during the past two decades. His book contributes to the rapidly developing literature on the macroeconomic consequences of market power. I argue that Philippon fails to convincingly support his hypothesis because (1) he does not consider competition as a process operating on several margins, with the price being only of them, and (2) the available empirical data does not unambiguously show a rise in market power. Philippon’s work suffers from similar flaws as the “structure-conduct-performance” paradigm. I also incorporate recent attempts to estimate aggregate market power and explain why the methods used for such estimations are calculated by making assumptions over which margins entrepreneurs compete and suffers from serious knowledge problems. Finally, I offer an alternative view of competition, which focuses on how and at what speed rents are being dissipated instead of the markup of price over marginal cost.

Could Loss Aversion Retain on the Market? Evidence from the Hong Kong Property Market

Loss aversion not only affects the list price of properties but can retain on actual transactions. Utilizing the data of over a million commercial and residential property transactions in Hong Kong from 1991 to 2015, we find that sellers facing nominal losses relative to their prior purchase prices attained higher selling prices than their counterparts. We suggest two market factors to account for the extent of the loss aversion effect on the market transaction prices. First, the loss aversion effect is only prominent when comparable transaction information is not readily accessible, such as in the less-transacted commercial property market. Second, our results suggest the relevance of loss aversion to the boom-bust property cycle in both the residential and commercial markets. The effect of loss aversion on transaction prices is relatively weak in the bust period between 1998 and 2003 when the Hong Kong property market lost almost two-thirds of its value, and it enlarges with the market recovering. The power of loss aversion is not attenuated at the aggregate market level but is associated with strong reductions in price declines in the bust period and in the commercial market. These results have implications for understanding the market adjustment of loss aversion in different property sectors and its association with the aggregate market dynamics in a boom-bust property cycle.

Uncertainty Resolution Before Earnings Announcements

We document that 72% of the earnings announcement premium is realized before, rather than after, earnings releases. It is possible that the large pre-announcement returns are compensations for uncertainty resolution before announcement. To test the uncertainty resolution hypothesis, we examine the pattern of pre-announcement returns in the cross section of stocks. There are compelling empirical evidence in support of the hypothesis: the pre-announcement returns are significantly higher for firms with high uncertainty, and when the aggregate market uncertainty is high. Finally, we present two distinct channels for uncertainty resolution: information acquisition by investors and information supply by analysts.

Gold, platinum, and expected Bitcoin returns

This study examines the prediction power of the ratio of gold to platinum prices (GP) on Bitcoin. By using different models and data sources for Bitcoin, we find that GP predicts future Bitcoin return. When the price of gold relative to platinum increases, Bitcoin return also goes up. This finding is consistent with previous studies and contributes to the ongoing discussion whether GP can be used as an indicator for aggregate market risk. Using variance decomposition, we also find that volatility in the gold and platinum market can influence Bitcoin volatility and that this relationship shows time-varying dependency. Finally, our results are robust to including new year effects and data of different frequency. Hence, this paper contributes to the current literature of Bitcoin by showing that GP provides an important proxy of risk in the economy.

Competition Is (Still) a Tough Weed: A Review Essay of Thomas Philippon’s the Great Reversal

In The Great Reversal, Philippon makes the case that competition in America has weakened during the past two decades. His book contributes to the rapidly developing literature on the macroeconomic consequences of market power. I argue that Philippon fails to convincingly support his hypothesis because (1) he does not consider competition as a process operating on several margins, with the price being only of them, (2) The available empirical data does not unambiguously show a rise in market power. Philippon's work suffers from similar flaws as the structure-conduct-performance paradigm. I also review the recent attempts to estimate aggregate market power and explain why the methods used for such estimations are calculated by making assumptions over which margins entrepreneurs compete and suffer from serious knowledge problems. Finally, I offer an alternative view of competition which focuses on how and at what speed rents are being dissipated instead of the markup of price over marginal cost.

Constructing a partially formalized general theory of the marketing system: insights from the history of marketing thought

PurposeThe purpose of this paper is to construct a general theory of the marketing system that addresses the fundamental question: why do marketing systems occur, survive and grow?Design/methodology/approachThe approach integrates the concepts and constructs contained in special and mid-range theories, scattered throughout the history of marketing thought, into a logically coherent set of propositions (including definitions, axioms, theorems, scientific laws, bridge laws and hypotheses) that comprise a general theory of the marketing system.FindingsThe theoretical answer to why marketing systems arise, survive and grow is because marketing systems offer the most efficient mechanism for supplying products and services that people demand, thereby increasing economic growth, compared to the opportunity costs of alternative methods of acquisition. Based on just two (of several) marketing efficiency theorems, if the input costs of trading decline (law of reduced transaction costs) and/or the output value increases (law of bulk transactions), then marketing system efficiency rises. This creates an upward spiraling cycle: increasing the extent of the market (law of market size), proliferating opportunities for increasing aggregate production efficiency (through the law of comparative advantage and the law of division of labor), thereby further proliferating opportunities for aggregate marketing system efficiency (e.g. law of central markets, law of marketing specialists), thus fueling further aggregate economic growth (until limited by the law of diminishing returns, the law of the minimum resource or the law of market size). An empirically testable central hypothesis is derived from the propositions: increasing aggregate marketing system efficiency provides both the necessary and sufficient conditions for increasing aggregate economic growth in a society.Originality/valueThe value of developing a general theory of the marketing system is to advance the marketing discipline as a social science. Additionally, a general theory is likely to enhance academic thinking, improve business practice and facilitate interaction among academicians and practitioners. Further, a general theory could also reduce disciplinary fragmentation, avoid identity confusion and lessen the credibility crisis in marketing, among others.