advantages of each strategy separately without attempting integration. The aim of this study was to test the

effects of combining these two strategies into a dynamic approach, using Ichimoku as a mediator. This

quantitative research uses daily stock prices taken from the Indonesia Stock Exchange website to analyze the

mechanism of the relationship between heuristics and investment performance. Our research demonstrates the

superior performance of the Dynamic strategy in generating higher returns when compared to alternative

strategies. One of the main reasons behind this success is how well Ichimoku can navigate this indirect

influence. The proposed model demonstrates strong predictive abilities and sets itself apart from the strategies

that influence its development. Investors of all backgrounds, including individuals, can easily integrate this

innovative strategy. Therefore, the study makes a valuable contribution to improving investment strategies,

making them more comprehensive and effective.

1 Introduction

Investment judgments often rely on anchor and

representative heuristics, particularly when technical

analysis lacks robust information support. These

heuristics assist in simplifying intricate situations

and rapidly assessing the likelihood of events under

time limitations, [1]. The anchor and adjustment

heuristics involve using initial reference values and

modifying them based on further information [2],

assessing how closely an event matches a particular

category. Investors often anticipate success with

high-performing stocks and frequently rely on

representativeness heuristics to develop trading

strategies, [5]. Understanding heuristics in

direction pattern, [9].

researchers have obtained evidence regarding the

reliability of momentum strategies, namely

strategies that buy shares that perform well and sell

shares that perform poorly, in generating excess

returns, [10], [11], [12], [13], [14]. Conversely,

some traders view stocks that are near 52-week

highs (lows) as indicators of stocks with over- (or

under-) performance. This view, however, often

stocks, can offer superior returns, [17], [18], [19],

[20], [21], [22]. This observation highlights a

significant gap in the field's knowledge and

underscores the importance of understanding how

traders employ heuristics to craft more precise

investment strategies when making investment

decisions. To maximize returns for investors, such

schemes must carefully consider behavioral biases

and market dynamics.

one approach over another without considering that

integration is often the best approach. However,

these obstacles have hampered our ability to study

how market dynamics interact with investment

outcomes. One distinct limitation is that, with a few

exceptions distinct limitation is that, with a few

exceptions, [23]. The literature does not offer

detailed causal mechanisms to explain how

heuristics can impact investment performance.

manner, with Ichimoku as the mediator. Despite its

prognostic capabilities, Ichimoku remains relatively

unexplored. By adopting an integrative

methodology, this research paper presents a new

viewpoint that highlights the benefits of dynamic

Fig. 1: Encapsulates the research model, outlining

the hypothesized relationships among Momentum,

Contrarian, Ichimoku, and the resulting Dynamic

complete data on these equities, taking into account

dividends and stock splits. The return calculation

will begin on January 2, 2018. This comprehensive

data set provides a solid foundation for careful study

making abilities have the potential to lead to greater

profits and success in the financial markets.

compute Ichimoku elements bear conceptual

resemblances to conventional moving averages. The

method employs a mid-point moving average. The

Tenkan-sen (TK) represents short-term moving

averages, whereas the Kijun-sen (KJ) represents

medium-term moving averages. Senkou-span A

(SKA) and Senkou-span B (SKB) might be likened

9, 26, and 52. This systematic approach contributes

to Ichimoku's efficacy as a technical analysis tool

that is robust and adaptable, [27], [28].

trend, whereas a crossover from the top to the

provides insights into the trend's strength. In a

bullish trend, a bottom-to-top breakout (penetration)

of the cloud signifies a robust trend. Conversely, in

a bearish trend, a top-to-bottom penetration of the

cloud indicates a strong bearish trend. The Chikou-

span is employed to confirm trend formation.

3.4 Trading Strategy

Guided by the NR formula, the Momentum strategy

value exceeding 70% and selling shares with an NR

research period and  is the closing price at the

beginning of the research period. Accordingly, there

is the excess return (󰇜 , i.e. the difference between

the return of each strategy and the return

This study presents a new dynamic trading strategy

direct and indirect impacts. Direct impact relates to

the direct influence of each strategy on the dynamic

strategy, while indirect impact includes the

influence of each strategy on the dynamic strategy

through Ichimoku.

3.7 Confirmatory Composite Analysis

(CCA)

Confirmatory composite analysis (CCA) was used

to assess the quality of measurement models in PLS-

exclusively employs formative measurements, the

evaluation comprises: (1) scrutinizing the formative

measurement model by analyzing redundancy,

variance inflation factor (VIF), and the significance

and relevance of indicator weights. (2) Assessing

the structural model through VIF examination,

alongside scrutinizing the significance and

relevance of path coefficients, explanatory and

predictive power, and evaluating the goodness of fit

through relevant measures. Given the non-linear

indicating a prevalence of lower negative returns.

For the Ichimoku Strategy, returns are clustered

between 0% and 600%, with a positive skewness

(1.366), indicating a preference for higher positive

returns. Meanwhile, the Dynamic Strategy displays

a wider range of returns, centered around 200% to

800%, and the highest positive skewness (1.647),

highlighting a strong inclination towards higher

positive returns. These insights enable investors to

assess the risk and return characteristics of each

strong and positive relationship between the

Ichimoku variable (ICH) and dynamic strategy

(DYN). Additionally, a significant positive path

coefficient for MOM → ICH (0.407) and a

ICH (-0.196) indicate that MOM and CON

significantly influence the use of Ichimoku in the

context of stock trading.

Table 3. Direct effects and indirect effects of

4.5 Normality Test

Table 5 displays significant deviations from the

significant findings (H=223.704) with 3 degrees of

freedom and a p-value of 0.000, signifying

significant rank variations among the strategies.

This variation suggests statistically significant

differences in Excess Return across all strategies.

studies have additionally confirmed the capacity of

Ichimoku to identify the commencement and

deduction of trends, [37], [38]. Similar to the

moving average method, Ichimoku is effective in

filtering out market noise and accurately identifying

trends. Its breakout methodology allows the initial

identification and precise conclusion of trends in

line with established technical trading principles,

[39], [40] The evidence substantiating the

superiority of the Ichimoku Strategy over the

investigation is Dynamic Strategy, as indicated by

the results of the comprehensive analytics. For

market practitioners, the Dynamic Strategy presents

a promising approach, as it generates significantly

higher average excess returns than the Momentum,

Contrarian, and Ichimoku Strategies. The value of

10,717 clearly exemplifies the advantage of its

significant volatility. Market practitioners can

profitably optimize their return on investment by

strategies exhibited unexpected and robust direct

effects in the opposite direction than anticipated.

Relationship Figure A2.1 and Figure A2.2

(Appendix) show the relationship between

momentum, contrarian, and dynamic strategies. This

shows that price movements follow an "S-curve"

shape, which means that prices may go backward

and forward around the highs and lows in a short

period. Both strategies make a weak contribution to

the influence of the dynamic strategy. Even

gaining a deeper understanding of market dynamics,

which includes recognizing the impact of trader

noise and identifying anomalies such as

continuation and reversal patterns. The report

emphasizes the need to modify trading strategies in