SOCIO-ECONOMIC VOICES

"The Impact of Recent Capital Gain Tax Reforms on Investors' Behaviour in India"
-Anubha Srivastava,Ph.D., UGC NET, M. Com, CertIFR (ACCA)

1- Introduction-

Capital gains tax is a type of taxes that are imposed on the sale of capital asset like stock, bonds, real estate and investment. The capital gains are realized when there is an increase in selling price and purchase price of the asset leading to tax. Capital gains are typically divided into two groups, namely short-term capital gains (STCG) and long-term capital gains (LTCG).

The rate of taxation on capital gains varies between different countries and the nature of asset. In most of the nations, short-term capital gains are taxed higher and usually at the same rate as ordinary income tax, with long-term capital gains being taxed preferentially to encourage long-term investment at reduced rates. Certain jurisdictions offer discounts, deductions or index benefits to compensate inflation and cut taxable benefits. Also, the capital gain tax policies can have clauses of tax-deferred or rollover relief where reinstatement in a particular asset can delay tax payments.

(Niels V et. al., 2007) found that the elasticity of revenues with respect to the tax rate over 10 years is –0.5 to –0.3, indicating that capital gains tax cuts do not pay for themselves and that a 5-percentage point rate increase would yield $18 to $30 billion in annual federal tax revenue. OCapital profit tax policies are commonly used by governments to control the behavior of investment and economic activity. An increase in capital gains rates can keep short-term speculation off and encourage long-term money accruals whereas a reduction in capital gains rates will encourage an increase in involvement in the financial market. Tax laws are regularly changed according to the interests of the economy and this demands that investors should be aware of the changes to align their tax strategies and remain conscious of the changes.

The capital gains in India are divided into two, namely short -term capital gains (STCG) and long -term capital gains (LTCG) in accordance with the holding period of the asset. The holding period will differ according to the type of property; Equity and equity mutual funds will be long-term when they are held longer than 1 year, whereas real estate and debt mutual funds will need more than two years and three years respectively. Is, long -term to qualify. The capital gains tax policies are usually policies employed by governments to manipulate financial markets and investor behaviour. Policymakers can use the tax rates on capital gains to either promote or deter some form of investment activities.

The capital gains tax policy changes also have an effect on the asset allocation, as well as liquidity in financial markets. Increase in the tax on long term gains will force investors to invest in other forms of investments like bonds, gold and real estates to avoid paying the tax and this will slow down the liquidity in the stock market. On the same note, a reduced tax on long run performances may boost long run investment causing the growth and stability of the market.

Besides, the investor sentiment is closely associated with changes in taxation. The market sell-offs can occur due to sudden rises in the amount of capital gains tax since investors will scramble to ensure that they secure profit before the new tax rate is applied. Conversely, the tax cuts can be used to stimulate economic growth by expanding the post-tax returns, which are used to stimulate the trading and investment volumes.

The Finance Minister announced major tax reforms related to capital gain tax in the 2024-2025 that stressed both tax simplification and promoted long-term financial investments. A major change in the taxation structure concerns the STCG tax rate increase from 15% to 20% which applies to both listed equity shares along with equity-oriented mutual funds. The Indian government implemented this policy to limit speculative trading activities and extend the investment duration. LTCG taxation applies at a 12.5% flat rate for all assets following the elimination of previous heterogenous taxation. All listed financial assets which satisfy more than one-year holding period will now qualify for long-term capital gain tax treatment.

2-Literature Review-

(Saxton (1997) highlighted in their work that there is bias in the current tax system against saving, investment, emphasizing the various taxation of capital gains under the existing income tax regulation.

(L. Olivier, 2007) contributed to discussions on tax reform, emphasizing the need for clearer regulations and simplification of tax laws to enhance compliance and reduce uncertainty in South Africa’s tax system. (George M. 1984) highlighted that tax laws incentivize investors to defer capital gains while realising losses promptly, particularly favouring short-term loss realization due to lower tax rates on long-term gains. (C. West and J. Roeleveld ,2003), highlighted interpretational challenges in their study and suggested legislative amendments, their research contributed to broader discussions on tax clarity and compliance, advocating for a more structured approach to legislative intent and practical tax application. (James R. and D. Schaffa, 2023) observed effects of capital gains taxes likely reflect investor anticipations of future tax rate changes, rather than the time value of money.

(Peter H et al, 2009 ) evaluated how taxes and stochastic interest rates influence both the timing option value and equilibrium price of corporate bonds through analysis of discount and premium amortisation and multiple trading dates, as well as transaction costs and changes in interest rates’ level and volatility. (Ole A and Zidar, 2020) revealed that capital gains tax revenue shows a negative relationship with tax rates encompassing -0.5 to -0.3 elasticity throughout ten years which proves tax reduction fails to entirely fund its own cost. The research analysis concluded that increasing capital gains tax rates by five percentage points would generate an extra $18 to $30 billion in yearly federal tax collections.

(Jennifer L. et., all., 2003), their research found out that personal capital gains taxes affect equity trading, adding to a growing literature that challenges longstanding assumptions that firm value is independent of shareholders and their taxes. (Holt and Shelton, 1992) investigated the behavioural reactions from individual investors compared to institutional investors since individual investors actively respond to tax incentives and established that tax policy modifies market behavior and investment choices, particularly during crucial tax periods, according to their study.

3- Research Methodology- This research examines the recent capital gains tax reforms on investor conduct in India. The research objectives for this study are:

  1. To examine the impact of changes in tax rules on Indian investor behavior.
  2. To examine the impact of changes in tax policy on the holding period of the Indian investor
  3. To investigate whether investors shift to other asset classes due to tax hikes.
  4. To assess the overall behavioral change in investors behavior due to chnages in capital gains tax regulations.

This investigation will exclude both corporate investors and high-net-worth individuals because professional tax planning allows them unrestricted access and their investment behaviors differ from retail investors.

The research depends on survey data collection through a questionnaire which aims to capture reactions from retail investors regarding modifications in tax policies. The target sample size is 200-300 respondents.

4- Data Analysis- Descriptive Details

Table 4.1 – Independent Variable – Change in Tax
  Statistic Std. Error
iv1 Mean 3.71 .068
95% Confidence
Interval for Mean
Lower Bound 3.57  
Upper Bound 3.84  
5% Trimmed Mean 3.74  
Median 4.00  
Variance .763  
Std. Deviation .873  
Minimum 1  
Maximum 5  
Range 4  
Interquartile Range 1  
Skewness -.543 .188
Kurtosis -.041 .374
iv2 Mean 3.84 .067
95% Confidence
Interval for Mean
Lower Bound 3.71  
Upper Bound 3.98  
5% Trimmed Mean 3.89  
Median 4.00  
Variance .759  
Std. Deviation .871  
Minimum 1  
Maximum 5  
Range 4  
Interquartile Range 0  
Skewness -.798 .188
Kurtosis .435 .374
iv3 Mean 3.80 .069
95% Confidence
Interval for Mean
Lower Bound 3.66  
Upper Bound 3.93  
5% Trimmed Mean 3.85  
Median 4.00  
Variance .790  
Std. Deviation .889  
Minimum 1  
Maximum 5  
Range 4  
Interquartile Range 1  
Skewness -.787 .188
Kurtosis .745 .374
iv4 Mean 3.75 .068
95% Confidence
Interval for Mean
Lower Bound 3.62  
Upper Bound 3.89  
5% Trimmed Mean 3.78  
Median 4.00  
Variance .765  
Std. Deviation .874  
Minimum 2  
Maximum 5  
Range 3  
Interquartile Range    
Skewness -.593 .188
Kurtosis -.207 .374
iv5 Mean 3.54 .085
95% Confidence
Interval for Mean
Lower Bound 3.37  
Upper Bound 3.71  
5% Trimmed Mean 3.56  
Median 4.00  
Variance 1.202  
Std. Deviation 1.096  
Minimum 1  
Maximum 5  
Range 4  
Interquartile Range 1  
Skewness -.364 .188
Kurtosis -.945 .374

Source- Author’s own compilation

As per the criteria, since the value of skewness and kurtosis values are between designated parameters, independent variable is norma

Dependent Variable

Participation in stock market (dv11, dv12, dv13, dv14, dv15)

Investment Holding period (dv21, dv22, dv23, dv24, dv25)

Shift in Asset Preference (dv31, dv32, dv33, dv34, dv35)

Table 4.2 – Dependent Variables – Descriptive Table
  Statistic Std. Error
dv11 Mean 3.50 .072
95% Confidence
Interval for Mean
Lower Bound 3.36  
Upper Bound 3.64  
5% Trimmed Mean 3.52  
Median 4.00  
Variance .854  
Std. Deviation .924  
Minimum 1  
Maximum 5  
Range 4  
Interquartile Range 1  
Skewness -.687 .188
Kurtosis -.011 .374
dv12 Mean 3.33 .062
95% Confidence
Interval for Mean
Lower Bound 3.21  
Upper Bound 3.45  
5% Trimmed Mean 3.39  
Median 4.00  
Variance .644  
Std. Deviation .802  
Minimum 1  
Maximum 4  
Range 3  
Interquartile Range 1  
Skewness -.951 .188
Kurtosis .073 .374
dv13 Mean 3.17 .086
95% Confidence
Interval for Mean
Lower Bound 3.00  
Upper Bound 3.34  
5% Trimmed Mean 3.19  
Median 3.00  
Variance 1.237  
Std. Deviation 1.112  
Minimum 1  
Maximum 5  
Range 4  
Interquartile Range 2  
Skewness -.364 .188
Kurtosis -.596 .374
dv14 Mean 3.41 .072
95% Confidence
Interval for Mean
Lower Bound 3.27  
Upper Bound 3.56  
5% Trimmed Mean 3.43  
Median 4.00  
Variance .870  
Std. Deviation .933  
Minimum 1  
Maximum 5  
Range 4  
Interquartile Range 1  
Skewness -.374 .188
Kurtosis -.231 .374
dv15 Mean 3.04 .077
95% Confidence
Interval for Mean
Lower Bound 2.89  
Upper Bound 3.19  
5% Trimmed Mean 3.05  
Median 3.00  
Variance .992  
Std. Deviation .996  
Minimum 1  
Maximum 5  
Range 4  
Interquartile Range 2  
Skewness -.122 .188
Kurtosis -.704 .374
dv21 Mean 3.49 .078
95% Confidence
Interval for Mean
Lower Bound 3.33  
Upper Bound 3.64  
5% Trimmed Mean 3.54  
Median 4.00  
Variance 1.010  
Std. Deviation 1.005  
Minimum 1  
Maximum 5  
Range 4  
Interquartile Range 1  
Skewness -.769 .188
Kurtosis .063 .374
dv22 Mean 3.22 .077
95% Confidence
Interval for Mean
Lower Bound 3.06  
Upper Bound 3.37  
5% Trimmed Mean 3.23  
Median 4.00  
Variance .977  
Std. Deviation .989  
Minimum 1  
Maximum 5  
Range 4  
Interquartile Range 2  
Skewness -.370 .188
Kurtosis -1.127 .374
dv23 Mean 3.47 .069
95% Confidence
Interval for Mean
Lower Bound 3.33  
Upper Bound 3.60  
5% Trimmed Mean 3.46  
Median 4.00  
Variance .793  
Std. Deviation .890  
Minimum 2  
Maximum 5  
Range 3  
Interquartile Range 1  
Skewness -.599 .188
Kurtosis -.814 .374
dv24 Mean 3.43 .083
95% Confidence
Interval for Mean
Lower Bound 3.27  
Upper Bound 3.60  
5% Trimmed Mean 3.48  
Median 4.00  
Variance 1.162  
Std. Deviation 1.078  
Minimum 1  
Maximum 5  
Range 4  
Interquartile Range 2  
Skewness -.725 .188
Kurtosis -.467 .374
dv25 Mean 3.80 .062
95% Confidence
Interval for Mean
Lower Bound 3.68  
Upper Bound 3.92  
5% Trimmed Mean 3.84  
Median 4.00  
Variance .641  
Std. Deviation .801  
Minimum 1  
Maximum 5  
Range 4  
Interquartile Range 1  
Skewness -.695 .188
Kurtosis .672 .374
dv31 Mean 3.28 .078
95% Confidence Interval for Mean Lower Bound 3.13  
Upper Bound 3.44  
5% Trimmed Mean 3.27  
Median 3.00  
Variance 1.011  
Std. Deviation 1.005  
Minimum 1  
Maximum 5  
Range 4  
Interquartile Range 2  
Skewness -.050 .188
Kurtosis -.990 .374
dv32 Mean 3.33 .082
95% Confidence
Interval for Mean
Lower Bound 3.17  
Upper Bound 3.49  
5% Trimmed Mean 3.35  
Median 4.00  
Variance 1.126  
Std. Deviation 1.061  
Minimum 1  
Maximum 5  
Range 4  
Interquartile Range 2  
Skewness -.294 .188
Kurtosis -.801 .374
dv33 Mean 3.26 .083
95% Confidence
Interval for Mean
Lower Bound 3.10  
Upper Bound 3.43  
5% Trimmed Mean 3.26  
Median 4.00  
Variance 1.147  
Std. Deviation 1.071  
Minimum 1  
Maximum 5  
Range 4  
Interquartile Range 2  
Skewness -.216 .188
Kurtosis -1.176 .374
dv34 Mean 3.32 .072
95% Confidence
Interval for Mean
Lower Bound 3.18  
Upper Bound 3.47  
5% Trimmed Mean 3.33  
Median 4.00  
Variance .871  
Std. Deviation .933  
Minimum 1  
Maximum 5  
Range 4  
Interquartile Range 1  
Skewness -.464 .188
Kurtosis -.538 .374
dv35 Mean 3.43 .080
95% Confidence Interval for Mean Lower Bound 3.27  
Upper Bound 3.58  
5% Trimmed Mean 3.45  
Median 4.00  
Variance 1.077  
Std. Deviation 1.038  
Minimum 1  
Maximum 5  
Range 4  
Interquartile Range 1  
Skewness -.403 .188
Kurtosis -.647 .374

Source- Author’s own compilation

As per the criteria, since the value of skewness and kurtosis values are between designated parameters, all 3 dependent variables are normal. Normal distributions make mean-based test results a lot more accurate since parametric tests assume normality.

4.2 Reliability Test (Cronbach’s Alpha)

Reliability testing using Cronbach’s Alpha assesses the internal consistency of a survey or questionnaire, ensuring that multiple items measuring the same construct produce consistent results. Cronbach’s Alpha values range from 0 to 1, with higher values indicating greater reliability.

Table 4.3 - Change in Tax

Reliability Test

  N %
Valid 167 100.0
Cases Excludeda 0 .0
Total 167 100.0

Reliability Statistics

Cronbach’s Alpha N of Items
.752 5

Table 4.4 – Reliability Test for Participation in stock market

Case Processing Summary

  N %
Valid 167 100.0
Cases Excludeda 0 .0
Total 167 100.0

Reliability Statistics

Cronbach’s Alpha N of Items
.755 5

Table 4.5 - -Reliability Test for Investment Holding Period

Case Processing Summary

  N %
Valid 167 100.0
Cases Excludeda 0 .0
Total 167 100.0

Reliability Statistics

Cronbach’s Alpha N of Items
.756 5

Table 4.6 – Reliability Test for Shift in Asset Preference

Case Processing Summary

  N %
Valid 167 100.0
Cases Excludeda 0 .0
Total 167 100.0

Reliability Statistics

Cronbach’s Alpha N of Items
.825 5

Since the Cronbach’s Alpha for the independent and dependent variables clear the 0.7 cutoff, each variable is reliable. This means that the answers given by the sample are consistent. A Cronbach’s Alpha value above the cutoff range Reliability increases the credibility of study as responses accurately mirror investor behaviour.

4.3 Other Descriptive Statistics -

  N Minimum Maximum Mean Std. Deviation Skewness Kurtosis
Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error
iv1 167 1 5 3.71 .873 -.543 .188 -.041 .374
iv2 167 1 5 3.84 .871 -.798 .188 .435 .374
iv3 167 1 5 3.80 .889 -.787 .188 .745 .374
iv4 167 2 5 3.75 .874 -.593 .188 -.207 .374
iv5 167 1 5 3.54 1.096 -.364 .188 -.945 .374
dv11 167 1 5 3.50 .924 -.687 .188 -.011 .374
dv12 167 1 4 3.33 .802 -.951 .188 .073 .374
dv13 167 1 5 3.17 1.112 -.364 .188 -.596 .374
dv14 167 1 5 3.41 .933 -.374 .188 -.231 .374
dv15 167 1 5 3.04 .996 -.122 .188 -.704 .374
dv21 167 1 5 3.49 1.005 -.769 .188 .063 .374
dv22 167 1 5 3.22 .989 -.370 .188 -1.127 .374
dv23 167 2 5 3.47 .890 -.599 .188 -.814 .374
dv24 167 1 5 3.43 1.078 -.725 .188 -.467 .374
dv25 167 1 5 3.80 .801 -.695 .188 .672 .374
dv31 167 1 5 3.28 1.005 -.050 .188 -.990 .374
dv32 167 1 5 3.33 1.061 -.294 .188 -.801 .374
dv33 167 1 5 3.26 1.071 -.216 .188 -1.176 .374
dv34 167 1 5 3.32 .933 -.464 .188 -.538 .374
dv35 167 1 5 3.43 1.038 -.403 .188 -.647 .374
Valid N (listwise) 167                

The mean values for independents variable (IVs) were from 3.54. to 3.84 on the Likert scale which means that the respondents agreed moderately to high for the Likert scale. Correspondingly, the dependent variables’ (DVs) mean values vary from 3.04–3.80 having a moderate response pattern. The value with the lowest mean value (3.04 for DV15) means that the respondents tend to take a relatively neutral standpoint, and the value with the highest mean value (3.84 for IV2) shows that the respondents are prone to agree on this topic. The standard deviation (SD) values, mostly between 0.8 and 1.1, indicate a moderate spread in responses. A higher SD suggests greater variability in responses, meaning participants had more diverse opinions. A negative skew means that responses tend to cluster more towards the higher end of the scale, with fewer responses on the lower end. The most negatively skewed variable is DV12 (-0.951), indicating that respondents were more likely to select higher values, while DV31 (-0.050) is the closest to a symmetrical distribution. Although skewness is present, all values remain within an acceptable range (±1), meaning the deviation from normality is not extreme. Kurtosis varies from -1.176 to 0.745, which shows that most distributions are fairly normal, but a fair mixture of skewed on the side of the flatter and skewed on the side of the peaked distributions. A key indicator of normality is whether skewness falls within ±1 and kurtosis within ±2, both of which hold true for this dataset. This suggests that the data is approximately normally distributed, making it suitable for most parametric statistical analyses.

1) Retail Investor Participation

H0 (Null Hypothesis): Changes in tax policy do not significantly affect retail investor participation in the stock market.

H1 (Alternative Hypothesis): Changes in tax policy significantly impact retail investor participation in the stock market.

Table 4.8 – Correlation Test – Change in Tax and Retail Investor Participation in stock market
    iv1 iv2 iv3 iv4 iv5 dv11 dv12 dv13 dv14 dv15
iv1 Pearson Correlation 1 .628** .582** .576** .091 .279** .345** .045 .201** -.013
Sig. (2-tailed)   .000 .000 .000 .244 .000 .000 .566 .009 .863
N 167 167 167 167 167 167 167 167 167 167
iv2 Pearson Correlation .628** 1 .535** .471** .303** .224** .238** .027 .087 -.138
Sig. (2-tailed) .000   .000 .000 .000 .004 .002 .728 .263 .075
N 167 167 167 167 167 167 167 167 167 167
iv3 Pearson Correlation .582** .535** 1 .346** .126 .307** .137 -.081 .124 -.201**
Sig. (2-tailed) .000 .000   .000 .106 .000 .078 .298 .111 .009
N 167 167 167 167 167 167 167 167 167 167
iv4 Pearson Correlation .576** .471** .346** 1 .334** .428** .468** .334** .568** .212**
Sig. (2-tailed) .000 .000 .000   .000 .000 .000 .000 .000 .006
N 167 167 167 167 167 167 167 167 167 167
iv5 Pearson Correlation .091 .303** .126 .334** 1 .174* .448** .296** .382** .354**
Sig. (2-tailed) .244 .000 .106 .000   .024 .000 .000 .000 .000
N 167 167 167 167 167 167 167 167 167 167
dv11 Pearson Correlation .279** .224** .307** .428** .174* 1 .209** .124 .396** .141
Sig. (2-tailed) .000 .004 .000 .000 .024   .007 .112 .000 .069
N 167 167 167 167 167 167 167 167 167 167
dv12 Pearson Correlation .345** .238** .137 .468** .448** .209** 1 .545** .509** .480**
Sig. (2-tailed) .000 .002 .078 .000 .000 .007   .000 .000 .000
N 167 167 167 167 167 167 167 167 167 167
dv13 Pearson Correlation .045 .027 -.081 .334** .296** .124 .545** 1 .722** .331**
Sig. (2-tailed) .566 .728 .298 .000 .000 .112 .000   .000 .000
N 167 167 167 167 167 167 167 167 167 167
dv14 Pearson Correlation .201** .087 .124 .568** .382** .396** .509** .722** 1 .409**
Sig. (2-tailed) .009 .263 .111 .000 .000 .000 .000 .000   .000
N 167 167 167 167 167 167 167 167 167 167
dv15 Pearson Correlation -.013 -.138 -.201** .212** .354** .141 .480** .331** .409** 1
Sig. (2-tailed) .863 .075 .009 .006 .000 .069 .000 .000 .000  
N 167 167 167 167 167 167 167 167 167 167

The highest positive correlations are: iv4 (Change in Tax Structure) and dv14 (.568) – A strong relation between iv4 and dv14, as change in tax structure seem to motivate individuals to participate. Tax related factors and stock market involvement have a moderate correlation; iv5 and dv12 (.448). A significant positive correlation suggests that as tax policy changes, participation in stock markets increases or decreases accordingly.

Table 4.9 – Regression Test – Change in Tax and Retail Investor Participation in stock market

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .406a .165 .160 .624

a. Predictors: (Constant), iv

Coefficientsa

Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 1.715 .280   6.129 .000
iv .422 .074 .406 5.714 .000

a. Dependent Variable: dv1

Model Sum of Squares df Mean Square F Sig.
1 Regression 12.722 1 12.722 32.652 .000b
Residual 64.290 165 .390    
Total 77.013 166      

a. Dependent Variable: dv1
b. Predictors: (Constant), iv

The correlation analysis shows a significant positive correlation between changes in tax policy and retail investor participation in the stock market. This suggests that changes in tax policy influence investor behavior. This is further supported by an R² value of 0.165 which implies that the independent variable, or tax policy change, explains 16.5% of the variation in market participation. Since the p value is 0.000, we can confirm the model's statistical significance and the estimate is Beta 0.422 which indicate a moderate positive relationship between changes in tax policy and market participation. Given these findings, we reject the null hypothesis and accept alternative hypothesis.

Hypothesis 2

2) Investment Holding Period

H0: Changes in tax policy do not influence the investment holding period of retail investors.

H1: Changes in tax policy significantly affect the investment holding period of retail investors.

Table 4.10 - Correlation Test – Change in Tax and Investment Holding Period

  iv1 iv2 iv3 iv4 iv5 dv21 dv22 dv23 dv24 dv25
iv1 Pearson Correlation 1 .628** .582** .576** .091 .170* .395** .386** .129 .244**
Sig. (2-tailed)   .000 .000 .000 .244 .028 .000 .000 .097 .001
N 167 167 167 167 167 167 167 167 167 167
iv2 Pearson Correlation .628** 1 .535** .471** .303** .224** .067 .289** .091 .232**
Sig. (2-tailed) .000   .000 .000 .000 .004 .388 .000 .241 .003
N 167 167 167 167 167 167 167 167 167 167
iv3 Pearson Correlation .582** .535** 1 .346** .126 .179* .318** .494** .476** .544**
Sig. (2-tailed) .000 .000   .000 .106 .021 .000 .000 .000 .000
N 167 167 167 167 167 167 167 167 167 167
iv4 Pearson Correlation .576** .471** .346** 1 .334** .314** .431** .458** .241** .352**
Sig. (2-tailed) .000 .000 .000   .000 .000 .000 .000 .002 .000
N 167 167 167 167 167 167 167 167 167 167
iv5 Pearson Correlation .091 .303** .126 .334** 1 .073 .120 .352** .057 .321**
Sig. (2-tailed) .244 .000 .106 .000   .349 .122 .000 .464 .000
N 167 167 167 167 167 167 167 167 167 167
dv21 Pearson Correlation .170* .224** .179* .314** .073 1 .385** .365** .223** .067
Sig. (2-tailed) .028 .004 .021 .000 .349   .000 .000 .004 .387
N 167 167 167 167 167 167 167 167 167 167
dv22 Pearson Correlation .395** .067 .318** .431** .120 .385** 1 .556** .285** .244**
Sig. (2-tailed) .000 .388 .000 .000 .122 .000   .000 .000 .001
N 167 167 167 167 167 167 167 167 167 167
dv23 Pearson Correlation .386** .289** .494** .458** .352** .365** .556** 1 .567** .536**
Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000   .000 .000
N 167 167 167 167 167 167 167 167 167 167
dv24 Pearson Correlation .129 .091 .476** .241** .057 .223** .285** .567** 1 .685**
Sig. (2-tailed) .097 .241 .000 .002 .464 .004 .000 .000   .000
N 167 167 167 167 167 167 167 167 167 167
dv25 Pearson Correlation .244** .232** .544** .352** .321** .067 .244** .536** .685** 1
Sig. (2-tailed) .001 .003 .000 .000 .000 .387 .001 .000 .000  
N 167 167 167 167 167 167 167 167 167 167

Table 4.11 - Regression Test – Change in Tax and Investment Holding Period

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate
1 .525a .276 .271 .582

a. Predictors: (Constant), iv

ANOVAa

Model Sum of Squares df Mean Square F Sig.
1 Regression 21.249 1 21.249 62.837 .000b
Residual 55.796 165 .338    
Total 77.045 166      

a. Dependent Variable: dv2
b. Predictors: (Constant), iv

Coefficientsa

Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta t Sig.
1 (Constant) 1.445 .261   5.543 .000
iv .546 .069 .525 7.927 .000

a. Dependent Variable: dv2

The correlation analysis indicates a positive relationship between changes in tax policy and the investment holding period, thus, changes in tax policy tend to have positive impact on the length of the investment holding period. The analysis shows a significant relationship between changes in tax policy and the investment holding period for retail investors. The R-squared value of 0.276 indicates that tax policy changes explain 27.6% of the variation in the investment holding period, suggesting moderate predictive power. The ANOVA and p-value (0.000) further confirm the statistical significance of the model, meaning tax policy changes significantly impact the investment holding period. Given these findings, we reject the null hypothesis and accept alternative hypothesis.

Hypothesis 3

H0: There is no significant shift in asset preferences (equity to other asset classes) among retail investors due to changes in tax policy.

H1: Changes in tax policy led to a significant shift in asset preferences among retail investors.

Table 4.12 - Correlation Test – Change in Tax and Shift in Asset Preference
  iv1 iv2 iv3 iv4 iv5 dv31 dv32 dv33 dv34 dv35
iv1 Pearson Correlation 1 .628** .582** .576** .091 -.187* -.006 .102 .147 .138
Sig. (2-tailed)   .000 .000 .000 .244 .016 .943 .188 .059 .074
N 167 167 167 167 167 167 167 167 167 167
iv2 Pearson Correlation .628** 1 .535** .471** .303** -.184* .062 .089 -.138 -.160*
Sig. (2-tailed) .000   .000 .000 .000 .018 .424 .250 .076 .039
N 167 167 167 167 167 167 167 167 167 167
iv3 Pearson Correlation .582** .535** 1 .346** .126 -.111 .078 .126 .167* .023
Sig. (2-tailed) .000 .000   .000 .106 .154 .317 .104 .031 .772
N 167 167 167 167 167 167 167 167 167 167
iv4 Pearson Correlation .576** .471** .346** 1 .334** -.037 .042 .359** .120 .129
Sig. (2-tailed) .000 .000 .000   .000 .631 .588 .000 .122 .097
N 167 167 167 167 167 167 167 167 167 167
iv5 Pearson Correlation .091 .303** .126 .334** 1 .375** .525** .520** .259** .232**
Sig. (2-tailed) .244 .000 .106 .000   .000 .000 .000 .001 .003
N 167 167 167 167 167 167 167 167 167 167
dv31 Pearson Correlation -.187* -.184* -.111 -.037 .375** 1 .664** .457** .403** .479**
Sig. (2-tailed) .016 .018 .154 .631 .000   .000 .000 .000 .000
N 167 167 167 167 167 167 167 167 167 167
dv32 Pearson Correlation -.006 .062 .078 .042 .525** .664** 1 .639** .336** .419**
Sig. (2-tailed) .943 .424 .317 .588 .000 .000   .000 .000 .000
N 167 167 167 167 167 167 167 167 167 167
dv33 Pearson Correlation .102 .089 .126 .359** .520** .457** .639** 1 .318** .430**
Sig. (2-tailed) .188 .250 .104 .000 .000 .000 .000   .000 .000
N 167 167 167 167 167 167 167 167 167 167
dv34 Pearson Correlation .147 -.138 .167* .120 .259** .403** .336** .318** 1 .709**
Sig. (2-tailed) .059 .076 .031 .122 .001 .000 .000 .000   .000
N 167 167 167 167 167 167 167 167 167 167
dv35 Pearson Correlation .138 -.160* .023 .129 .232** .479** .419** .430** .709** 1
Sig. (2-tailed) .074 .039 .772 .097 .003 .000 .000 .000 .000  
N 167 167 167 167 167 167 167 167 167 167

Table 4.13 - Regression Test – Change in Tax and Shift in Asset Preference

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate
1 .223a .050 .044 .767

a. Predictors: (Constant), iv

ANOVAa

Model Sum of Squares df Mean Square F Sig.
1 Regression 5.092 1 5.092 8.652 .004b
Residual 97.117 165 .589    
Total 102.209 166      

a. Dependent Variable: dv3
b. Predictors: (Constant), iv

Coefficientsa

Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 2.328 .344   6.770 .000
iv .267 .091 .223 2.941 .004

a. Dependent Variable: dv3

Change in tax policy is reflected in a shift towards gold, debt instruments, mutual funds, and stocks by seeing in several significant positive correlations. However, a negative relationship of tax changes with returns on assets implies that tax changes might discourage investing in assets including real estate. This conclusion supported by the regression analysis. Although R² = 0.050, the model is statistically significant (p-value = 0.004), and the Beta coefficient of 0.223 is positive with a modest relationship. It implies that retail investors do react to changes in such tax policy and make a meaningful shift in the assets they prefer. We thus do not accept the null hypothesis.

Given these findings, we reject the null hypothesis and accept alternative hypothesis.

Table 4.14 - ANOVA Test – Shift in Asset Preference

  Sum of Squares Df Mean Square F Sig.
dv31 Between Groups 53.592 13 4.122 5.524 .000
Within Groups 114.181 153 .746    
Total 167.772 166      
dv32 Between Groups 48.115 13 3.701 4.081 .000
Within Groups 138.771 153 .907    
Total 186.886 166      
dv33 Between Groups 95.622 13 7.356 11.873 .000
Within Groups 94.785 153 .620    
Total 190.407 166      

The results reveal that those groups differ significantly (by tax policy perceptions) in the shift of asset preference. The p-value (0.000) < 0.05, hence the null hypothesis is rejected. The value of the F-statistic; F = 5.524 leads to the conclusion that the variance between groups is considerably greater than the variance within groups. In the case of alternative/traditional assets also, there is a significant difference between the groups. The value of p is 0.000 is smaller than one in a thin double line, or cannot be read as p = 0.000 as such the independent variable (tax policy perceptions) has a significant effect on the shift in asset preference for dv32. This result is further confirmed by the F Statistic of 4.081. Tax-efficient mutual funds or index funds also contains a statistically significant effect of the independent variable on the dependent variable (shift changes in the assets preference). Tax policy changes have a very strong effect on tax-efficient mutual funds or index funds, as the p-value = 0.000 is far below 0.05 and F-statistic = 11.873 is quite high. In conclusion, all three null hypotheses are rejected and the alternative hypothesis are accepted.

5- Conclusion

The present study analyses the effect of tax policy changes on retail investors' behavior in India focusing on participation in the stock market, the holding period of their investment and switching in their assets preferences in case of change in tax regime. By performing a thorough analysis through correlation, regression and ANOVA tests, the conclusion is that changes in Tax policy do affect the Investor behavior. Findings indicate that both increased tax rates who inclined retail investors to participate more and to engage in longer holding periods and due to elevated tax rates, retail investors to prefer tax golf investments, tilting the results to investments exposed to indirect tax hikes.

The independent variable explains the variation moderately but the statistical significance and positive correlations lead to the rejection of the null hypothesis and support of alternative hypotheses. The implications can bring significant implication to the policymakers, specifically, the understanding of the effects of tax reforms on retail investors can inform policymakers to implement policies that will push towards particular investment pattern, market participation and diversification of the portfolio of the investors in a sustainable manner.

Lastly, the paper states the way of how a future research could be accomplished in terms of sectoral analysis, behavioral impact on investment decisions, country comparison, and long-term effects of tax change. Future investigators could replicate this analysis using other policy changes and investigate firms’ investment behaviour in different sectors. Focusing on a sector-by-sector basis could give more targeted insights to policymakers in order for them to implement industry specific taxes that would encourage investment without disturbing the markets.

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About Anubha Srivastava

Dr. Anubha Srivastava is an accomplished academician, researcher, and corporate trainer with over 13 years of experience in academia and training institutions across Indonesia, India, and Africa. Currently, she serves as a Consultant in Accounting and Finance for PT. DJerapah Magah Plasindha is in Indonesia and is a visiting faculty member at Universitas Diponegoro and UNNES. Dr. Srivastava has published over 28 research papers and served as a peer reviewer, editor, columnist, and keynote speaker at international conferences.

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indiastat.comDecember, 2025
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