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IMPROVING THE MECHANISM FOR STOCK PRICE FORMATION IN
THE STOCK MARKET: A MIXED APPROACH BASED ON GLOBAL AND
LOCAL CONTEXTS
Jumaboyev Mansur Akhmadjon ugli
Postgraduate (Master’s level) student of Tashkent State University of Economics,
Tashkent, Uzbekistan
Masharipov Ziyomat Ermat ugli
Postgraduate (Master’s level) student of Tashkent State University of Economics,
Tashkent, Uzbekistan
Kengesov Diyorbek Umidovich
Postgraduate (Master’s level) student of Karakalpak State University named after
Berdakh, Nukus, Uzbekistan
Abstract:
The article examines the theoretical and empirical aspects of stock
price formation, taking into account valuation models (DCF, CAPM, APT), as well
as the influence of market factors (volatility, liquidity) and institutional context
(regulation, role of exchanges, transparency of information). The analysis covers
global markets (USA, EU) and the market of the Republic of Uzbekistan. Examples
of significant events («dot-com bubble» 2008 financial crisis, 2020 COVID-19 crisis,
2010 flash crash) illustrate price distortions. Recommendations are being put forward
to improve the efficiency of the price formation process, including increasing
transparency, implementing consolidated transaction data, and improving trade rules.
Keywords:
stock price formation, market efficiency, behavioural finance,
hybrid pricing model, financial markets, Uzbekistan stock exchange, capital market
development, investor psychology, institutional reform, global stock markets.
Introduction.
The effectiveness of the stock market is largely determined by
how quickly and accurately the prices of shares reflect the real (fundamental) value of
companies. In the current conditions of technological development and market
globalization, improving the mechanisms for price disclosure and quotation
formation is becoming particularly important. Thus, in 2024, the EU adopted new
MiFID II/MiFIR rules aimed at increasing market transparency: unified consolidated
transaction feeds are being introduced so that investors have access to up-to-date data
on prices and volumes across the European Union [1]. In Uzbekistan, at the end of
2024, the integration of the Tashkent Exchange’s infrastructure with the Bloomberg
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platform was completed - now global investors can receive real-time data on the
trading of Uzbek stocks and bonds. According to NAPP officials, this should
«overcome barriers and create a dynamic, stable, and competitive capital market».
Simultaneously, in the US and EU markets, regulators are reviewing trading
practices: for example, a ban on «Payment for Order Flow» in the EU has been
introduced, which is designed to eliminate conflicts of interest between brokers and
improve pricing. Thus, regulatory and technological development tools emphasize
increasing the transparency and effectiveness of price formation processes.
The purpose of the research is to provide a comprehensive analysis of the
mechanisms of stock price formation, taking into account theoretical models,
empirical facts, and institutional conditions; on this basis, to formulate
recommendations for their improvement. For this, a theoretical approach (assessment
models, CAPM, APT, etc. ) is combined with an empirical analysis of the dynamics
of quotations and volatility, as well as a detailed analysis of events leading to
significant price deviations. Special attention was paid to the comparison of
developed US/EU markets and the young market of Uzbekistan.
Literature Review.
The classical theory of stock pricing is based on two
approaches. The first is future cash flow discounting (DCF), where the value of a
share is estimated as the present value of the expected dividends or free cash flows of
the firm. The DCF model requires reliable growth forecasts and discount rates, but
the results are very sensitive to these assumptions. The second approach is capital
assets valuation models. The basic CAPM (William Sharpe, John Lintner) states that
the expected return on a share depends only on its risk β relative to the market
portfolio. CAPM is based on strict assumptions (simultaneity of information, risk-
free borrowing at a single rate, liquidity, etc. ), and in practice, they are often not
implemented [2]. Moreover, empirical studies have revealed a number of anomalies
that cannot be explained by CAPM. Thus, Sanjay Basu (1977) showed that stocks
with high return/price (low P/E ratio) yield much more profit than CAPM predicted.
Rolf W. Banz (1981) discovered the «size effect» - low-capitalized companies
demonstrate higher returns [3]. Later, Peter Chung, Herb Johnson and Michael Schill
on Fama and French’s 1995 CAPM findings formulated a three-factor model, adding
to the market factor the factors of «cost» («price/balance sheet» or «income/price»
ratio) and «size» of the company. These factors significantly improve the explanation
of stock yield differences by identifying behavioural components: for example, when
comparing stocks with high and low P/E, it often turns out that highly valued «growth
stocks» are overvalued while «value stocks» are undervalued [4]. Thus, financial
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ratios and psychological effects (excessive optimism during bullish periods and
excessive pessimism during crises) lead to price distortions that do not account for
the classical CAPM.
An alternative to CAPM is the arbitration pricing theory (APT, Ross 1976). APT
allows for multiple factors and relates the expected return on an asset to a linear
combination of macroeconomic variables (inflation, GDP, interest rates, etc. ) and
market factors [5]. According to APT, if the share price deviates from the model's
corresponding level, arbitration investors can bring it to a «fair» value. In practice,
APT offers a more flexible structure: the return on a share is the sum of the
contributions of many beta factors, not just one β. However, the real application of
APT is hindered by the need to identify relevant factors and assess their loads.
Nevertheless, this approach emphasized the role of a wide range of risks in pricing
and became the basis for modern multi-factor models.
Modern research on price formation also analyses market microeconomics
(market microstructure). From the perspective of this literature, price formation is the
result of the interaction of buy and sell orders (orders) on the exchange glass.
Numerous studies have shown that market liquidity (narrow spreads, trading volume)
improves the discovery of «true» prices, while its absence leads to disruptions: high
spreads and low volumes cause stronger «market fluctuations». For example, Amihud
and Mendelson (1986) and others found a direct relationship between liquidity and
profitability: stocks with lower turnover (low liquidity) usually have a higher «risk
premium» due to trading costs. This agrees with observations that in small
developing markets, rare trading leads to large price fluctuations [6]. In addition to
market factors, information «noises» - rumours, news, and others - also play a
significant role. Experiments show that even with an effective news flow, market
participants can react irrationally, creating temporary price distortions.
In general, literature indicates the complexity of simple idealization: real stock
prices are formed under the influence of fundamental expectations (DCF), risk factors
(CAPM/APT), and market conditions (liquidity, macro-trends). The price mechanism
creates discrepancies between instant market price and «domestic» value due to the
combination of market frictions and behavioural characteristics.
Research methodology.
For comprehensive analysis, theoretical and empirical
methods are used:
Theoretical analysis. The main models of stock valuation are considered: cash
flow discounting (DCF), CAPM, APT, and their expansion (multi-factor pricing
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models). Model assumptions and their limitations in the context of real markets are
analysed (the need for forecasting, the role of macro factors, etc. ).
Empirical data. Historical series of price indices (Dow Jones, S&P 500, Nasdaq,
European indices), volatility indicators (VIX index, etc. ), as well as data on market
volumes and liquidity are used. Market reactions to key events (economic shocks,
crises, corporate news) are analysed using «before and after» (event studies).
Comparative analysis of markets. A comparison of pricing and regulation
mechanisms is carried out using the examples of the US and EU markets (developed,
high-liquidity) and the Uzbek market (less developed, low-liquidity). Differences in
legislation, infrastructure, and participants are assessed.
Case study. Specific examples of price distortion are being studied: the dot-com
bubble at the end of the 1990s, the 2008 global financial crisis, the 2020 «corona
crisis» and the 2010 flash crash. These cases illustrate how traditional pricing
mechanisms fail during periods of high uncertainty and "overheating. "
Analysis and results.
Figure 1. Historical dynamics of the NASDAQ Composite index (1971-2021)
[7]
The historical dynamics of the NASDAQ Composite index (1971-2021) show a
sharp rise in technological boom in the 1990s and subsequent collapse in the early
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2000s (drop bubble). During the period of 1995-2000. NASDAQ grew by
approximately 800%, after which it lost 78% of its value by October 2002. Such
graphs confirm the unstable nature of price surges and their impact on market
volatility.
In the developed markets of the USA and the EU, sharp price collapses and
periods of increased volatility associated with systemic shocks regularly occur. Thus,
on May 6, 2010 (Flash Crash), the Dow Jones index fell by almost 9% in a matter of
minutes [8]. Additionally, during the 2007-2009 crisis (Great Recession), U. S. stocks
lost about 50-60% of their market value: Dow Jones Industrial Average decreased by
53% from October 2007 to March 2009. The peak of panic on the US and European
stock markets occurred in March 2020: global indices fell by 30-35% between
February 20 and March 23, 2020, due to fears of the COVID-19 pandemic [9]. The
VIX volatility index temporarily exceeded 80 (record), reflecting the market's
extreme reaction to uncertainty. Such events demonstrate that in times of crisis, the
price formation mechanism can «disrupt»: in volatile sales, liquidity tends to zero,
and the price is formed under conditions of a shortage of buyers [8].
On the other hand, the «ordinary» market recovers quickly after shocks thanks
to arbitrage mechanisms and central bank policies. For example, after the 2008
decline, as liquidity stabilized, it returned, and the indices began to rise. In response
to the flash crash, US regulators introduced Reg NMS (2007) rules and tightened
monitoring of algorithmic traders. In the EU, the MiFID I/II reforms were also aimed
at increasing competition and transparency: the aforementioned creation of
consolidated data streams reduces information asymmetry between participants.
Thus, the ban on «payment flow» (PFOF) is planned to be completely lifted in the
EU to eliminate hidden incentives for brokers [1]. In practice, these measures allow
for improved price signal quality: when participants have equal access to quotation
and volume information, the price becomes closer to the «fair» fundamental cost.
Also, in global markets, the behaviour of investors and hedge fund strategies
have an impact. Numerous works suggest changing the trading microstructure to
improve price quality: frequent periodic auctions (batch auctions) instead of a
continuous glass can reduce the dominance of high-frequency traders. Overall, global
experience shows that a combination of reliable information systems, effective
regulation, and diversified instruments (futures, options, ETFs) contributes to reliable
price formation. Nevertheless, systemic failures (panic, manipulation, technical
errors) periodically lead to significant discrepancies in market prices and fundamental
expectations.
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The securities market of Uzbekistan is still at an early stage of development. As
noted in the investors' analysis, Uzbekistan is considered a frontier market: here the
quotation index and liquidity are significantly lower than in developed economies,
but there is potential for growth as privatization and IPO occur. The Tashkent Stock
Exchange («Tashkent» RSE) includes about 103 companies and 157 issued
instruments (shares and bonds). The total market capitalization of Uzbekistan's shares
is estimated at approximately $5. 2 billion, which is insignificantly small compared to
developed markets (where the calculation goes to trillions). Due to low capitalization
and a limited number of participants, Uzbekistan experiences a constant liquidity
shortage: the widespread presence of «passive» buy-and-hold investors, high
brokerage fees, and weak trading infrastructure hinder the prompt adjustment of
prices [10]. Corporate reporting in the local market has long been characterized by
low transparency, which also repelled foreign investors.
In recent years, the situation has begun to change thanks to reforms. The
integration of Uzbekistan's exchange information into global systems (through
Bloomberg) makes quotations and application books visible to all participants
worldwide. According to experts, this should increase confidence and market
competitiveness: «Bloomberg‘s integration data will become an important tool for
attracting foreign investment». Furthermore, the introduction of online platforms for
trading and transparent transaction accounting contributes to reducing information
asymmetry. At the legislative level, the protection of investors’ rights has been
strengthened, and the attraction of foreign capital is being simplified: for example, in
recent years, restrictions on the share participation of non-residents in companies
have been reduced. In fact, there is an increase in the number of small transactions in
the market and an intensification of trade in narrow sectors.
However, certain risks remain. From the analysts’ text, it follows that the weak
legal and regulatory environment, as well as limited access to international settlement
systems (Euroclear, Clearstream), continue to isolate Uzbek trade from the global
market. Arbitration strategies here are still less effective due to low volumes - for
example, news about a macroeconomic event can cause a significant price «jump».
Examples of speculative surges of a local nature are still rare in the media, but it is
well known that during periods of regional shocks (changes in oil prices, sanctions,
pandemics), Uzbek stocks react more aggressively than stocks in developed markets
due to a combination of increased uncertainty and lack of buyers.
Thus, using the example of Uzbekistan, it can be seen that pricing mechanisms
are only beginning to reach an economically justified balance. Increased transparency
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(as Bloomberg does) is already showing positive effects, but further development of
institutions is required - improving reporting, reducing trade barriers, and expanding
the debt instruments market. Incentives for long-term investments (such as tax
incentives for investors) can help reduce speculative fluctuations and bring prices
closer to fundamental indicators.
Conclusion and recommendations.
Based on theoretical analysis and empirical
observations, the following conclusions and proposals can be formulated for
improving the mechanism for forming the value of shares:
Firstly, strengthening market transparency: Creating a unified consolidated
transaction and quotation feed allows each participant to see the full picture of trading
in real time. For example, combining exchange data through a single channel (as in
the EU) allows investors access to the current price and volume across all platforms.
It is also important to ensure a disclosure policy - publishing company reports in
accordance with international standards and promptly reporting on important
corporate events. These measures restrict information privileges and accelerate the
process of setting a «fair» price.
Secondly, development of the liquidity market: stimulating the activities of
market makers and institutional investors who contribute to market depth. For
example, the exchange can implement incentive mechanisms (reducing commissions,
compensation for market makers) to maintain narrow spreads. Uzbekistan is
recommended to continue expanding the presence of foreign investors (removing
administrative restrictions) and developing the forward market (futures, options),
which will provide additional currency and inter-market arbitrage relations.
Thirdly, improving trade rules: reviewing practices like «payment for a stream
of orders» that may distort participants' incentives. Introduce adequate stop-losses
and commercial «emergency brakes» (circuit breakers) to limit panic sales. Consider
the possibility of holding periodic auctions (batch auctions) at the opening/closing of
bidding to avoid situations where the price is set on an empty glass. These measures
reduce the likelihood of sudden flash crashes and improve the quality process of
discounting new information.
Fourthly, focus on qualitative assessment factors: encourage the analysis of
companies’ fundamental value and the implementation of advanced models (multi-
factor models, ESG factor accounting, etc. ). For example, institutions can conduct
regular assessments and forecasts based on DCF and profitability models, while
exchanges and regulators can teach investors how to understand financial ratios. This
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will help the market «check» excessive optimism/pessimism and equalize prices
closer to reasonable values.
Finally, supporting institutions and infrastructure: investing in trade
digitalization, information systems, and cybersecurity. Rapid and reliable data
exchange between exchanges, clearing centres, and the regulator accelerates the
process of finding market equilibrium. For Uzbekistan, continuing reforms (digital
regulatory sandboxes, improving the legal framework for securities) is relevant,
which will attract new issuers and increase the share of shares in the economy.
Thus, improving the mechanism for determining the price of shares requires a
comprehensive approach: a balanced combination of theoretical valuation models,
empirically justified regulatory decisions, and strengthening market institutions.
Ultimately, the goal is for the market price of shares to reflect their fundamental
value as quickly and reliably as possible, creating investor confidence and supporting
the sustainable development of the economy.
References:
1. Council of the EU. MiFIR and MiFID II: Council adopts new rules to
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Page 11.
4. Social Science Research Network (SSRN). «Asset Pricing When Returns Are
Nonnormal: Fama French Factors vs. Higher-order Systematic Co-Moments», Pages
1-3.
5. Ross, Stephen. The arbitrage theory of capital asset pricing // Journal of
Economic Theory: journal. — 1976. — Vol. 13, no. 3. — P. 341—360. — doi:10.
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6. Yakov Amihud, Haim Mendelson. Asset pricing and the bid-ask spread.
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0304-405X, https://doi. org/10. 1016/0304-405X(86)90065-6.
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svg&oldid=1001789004.
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8. Kirilenko, Andrei; Kyle, Albert S. ; Samadi, Mehrdad; Tuzun, Tugkan, The
Flash Crash: The Impact of High Frequency Trading on an Electronic Market, May 5,
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