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Many of the insights of economics seem to be qualitative, with many fewer reliable quantitative laws. However a series of power laws in economics do count as true and nontrivial quantitative laws—and they are not only established empirically, but also understood theoretically. I will start by providing several illustrations of empirical power laws having to do with patterns involving cities, firms, and the stock market. I summarize some of the theoretical explanations that have been proposed. I suggest that power laws help us explain many economic phenomena, including aggregate economic fluctuations. I hope to clarify why power laws are so special, and to demonstrate their utility. In conclusion, I list some power-law-related economic enigmas that demand further exploration. A formal definition may be useful.

We build a hybrid model of the aggregate labor market that features both standard labor supply forces and frictions in order to study the cyclical properties of gross worker flows across the three labor market states: employment, unemployment, and nonparticipation. Our parsimonious model is able to capture the hey features of the cyclical movements in gross worker flows. Despite the fact that the wage per efficiency unit is constant over time, intertemporal substitution plays an important role in shaping fluctuations in the participation rate.

Using individual patient records for every hospital in California from 1983 to 2011, we find a strong inverse link between daily stock returns and hospital admissions, particularly for psychological conditions such as anxiety, panic disorder, and major depression. The effect is nearly instantaneous (within the same day) for psychological conditions, suggesting that anticipation over future consumption directly influences instantaneous utility.

Search engine query data deliver insight into the behaviour of individuals who are the smallest possible scale of our economic life. Individuals are submitting several hundred million search engine queries around the world each day. We study weekly search volume data for various search terms from 2004 to 2010 that are offered by the search engine Google for scientific use, providing information about our economic life on an aggregated collective level. We ask the question whether there is a link between search volume data and financial market fluctuations on a weekly time scale. Both collective 'swarm intelligence' of Internet users and the group of financial market participants can be regarded as a complex system of many interacting subunits that react quickly to external changes. We find clear evidence that weekly transaction volumes of S&P 500 companies are correlated with weekly search volume of corresponding company names. Furthermore, we apply a recently introduced method for quantifying complex correlations in time series with which we find a clear tendency that search volume time series and transaction volume time series show recurring patterns.

For an intriguing variety of switching processes in nature, the underlying complex system abruptly changes from one state to another in a highly discontinuous fashion. Financial market fluctuations are characterized by many abrupt switchings creating upward trends and downward trends, on time scales ranging from macroscopic trends persisting for hundreds of days to microscopic trends persisting for a few minutes. The question arises whether these ubiquitous switching processes have quantifiable features independent of the time horizon studied. We find striking scale-free behavior of the transaction volume after each switching. Our findings can be interpreted as being consistent with time-dependent collective behavior of financial market participants. We test the possible universality of our result by performing a parallel analysis of fluctuations in time intervals between transactions. We suggest that the well known catastrophic bubbles that occur on large time scales—such as the most recent financial crisis—may not be outliers but single dramatic representatives caused by the formation of increasing and decreasing trends on time scales varying over nine orders of magnitude from very large down to very small.

The country’s economy is strongly influenced by investment, so it is important to identify the factors that determine investors’ choices to invest in certain areas, which means it is important to anticipate how to create favorable economic, social, legal and other investment conditions to attract investment. The situation of stock markets during COVID-19 has only once again shown the important role that stock markets play for national economies. Numerous scientific sources describe how stock markets work in relation to the global economy, but do not make enough suggestions or conduct sufficient research to decide how to successfully forecast stock markets in the face of increasing globalization. After the analysis of the scientific literature and the correlation analysis, the aim will be to identify the peculiarities of the signals affecting the behavior of the stock market, and what importance they may have in proper investment management. The study will use global annual growth rates for the healthcare and technology sectors and the annual return funds: SEB Medical Fund and SEB Technology Fund. The correlation analysis will use 5-year data to determine whether growth in different sectors can be signals in stock market forecasting and will be used in planned further research using artificial intelligence techniques. Article in Lithuanian. Akcijų rinkos elgseną veikiančių signalų ypatumų globalioje aplinkoje tyrimas Santrauka Šalies ekonomikai didelę įtaką daro investicijos, todėl labai svarbu identifikuoti, kokie veiksniai lemia investuotojų pasirinkimus investuoti į tam tikras sritis, o tai reiškia, kad svarbu numatyti, kaip sukurti palankias investavimo sąlygas, susijusias su ekonominiais, socialiniais, teisiniais ir kitais aspektais, siekiant pritraukti investicijų. Akcijų rinkų situacija COVID-19 metu tik dar kartą parodė, kokį svarbų vaidmenį valstybių ekonomikoms atlieka akcijų rinkos. Daugybėje mokslinių šaltinių aprašoma, kaip veikia akcijų rinkos, kaip susijusios su globalia ekonomika, tačiau nėra pateikiama pakankamai pasiūlymų, ar atlikta tiek tyrimų, kad būtų galima nuspręsti, kaip sėkmingai prognozuoti akcijų rinkas susiduriant su vis plačiau pasireiškiančiais globalizacijos procesais. Po atliktos mokslinės literatūros analizės ir koreliacinės analizės bus siekiama identifikuoti akcijų rinkos elgseną veikiančių signalų ypatumus, kokią svarbą jie gali turėti tinkamai valdant investicijas. Atliekant tyrimą bus remiamasi globaliais metiniais sveikatos apsaugos sektorių ir technologijų sektoriaus bei metinės grąžos fondų: ,,SEB Medical Fund“ ir ,,SEB Technology Fund“ – augimo tempais. Atliekant koreliacinę analizę, naudojami 5 metų duomenys siekiant nustatyti, ar skirtingų sektorių augimas gali būti signalas atliekant akcijų rinkų prognozes. Duomenys naudojami planuojamuose tolesniuose tyrimuose taikant dirbtinio intelekto metodus. Reikšminiai žodžiai: akcijų rinkos, investiciniai sprendimai, akcijų rinkų svyravimai, ekonomika, signalai.

Using empirical data from a social media site (Twitter) and on trading volumes of financial securities, we analyze the correlated human activity in massive social organizations. The activity, typically excited by real-world events and measured by the occurrence rate of international brand names and trading volumes, is characterized by intermittent fluctuations with bursts of high activity separated by quiescent periods. These fluctuations are broadly distributed with an inverse cubic tail and have long-range temporal correlations with a [Formula] power spectrum. We describe the activity by a stochastic point process and derive the distribution of activity levels from the corresponding stochastic differential equation. The distribution and the corresponding power spectrum are fully consistent with the empirical observations.

We study asset prices in an economy where investors derive direct utility not only from consumption but also from fluctuations in the value of their financial wealth. They are loss averse over these fluctuations, and the degree of loss aversion depends on their prior investment performance. We find that our framework can help explain the high mean, excess volatility, and predictability of stock returns, as well as their low correlation with consumption growth. The design of our model is influenced by prospect theory and by experimental evidence on how prior outcomes affect risky choice.

We study equilibrium firm-level stock returns in two economies: one in which investors are loss averse over the fluctuations of their stock portfolio, and another in which they are loss averse over the fluctuations of individual stocks that they own. Both approaches can shed light on empirical phenomena, but we find the second approach to be more successful: In that economy, the typical individual stock return has a high mean and excess volatility, and there is a large value premium in the cross section which can, to some extent, be captured by a commonly used multifactor model.

This paper studies differences in exposure to aggregate fluctuations across households, focusing on high consumption and high income households. This work studies differences in the covariation of consumption growth only with equity returns, and not with aggregate fluctuations more generally. This is significant since the share of aggregate income that comes from labor is roughly double the share coming from capital. This research is limited by underrepresentation of households with very high consumption in standard consumption datasets.